diff --git a/.github/workflows/test-linting.yml b/.github/workflows/test-linting.yml
index e2c6500b98..9638c00e45 100644
--- a/.github/workflows/test-linting.yml
+++ b/.github/workflows/test-linting.yml
@@ -61,7 +61,7 @@ jobs:
     - name: Run MyPy type checking
       run: |
         cd litellm
-        poetry run mypy . --ignore-missing-imports --disable-error-code=var-annotated
+        poetry run mypy . 
         cd ..
 
     - name: Check for circular imports
diff --git a/ci_cd/security_scans.sh b/ci_cd/security_scans.sh
index 897589c0a4..4255885bcb 100755
--- a/ci_cd/security_scans.sh
+++ b/ci_cd/security_scans.sh
@@ -66,18 +66,34 @@ run_grype_scans() {
     echo "Scanning locally built LiteLLM image for high-severity vulnerabilities..."
     echo "Using locally built image: litellm:latest"
     
-    # Run grype scan and check for vulnerabilities with CVSS >= 4.0
+    # Allowlist of CVEs to be ignored in failure threshold/reporting
+    # - CVE-2025-8869: Not applicable on Python >=3.13 (PEP 706 implemented); pip fallback unused; no OS-level fix
+    ALLOWED_CVES=(
+        "CVE-2025-8869"
+    )
+
+    # Build JSON array of allowlisted CVE IDs for jq
+    ALLOWED_IDS_JSON=$(printf '%s\n' "${ALLOWED_CVES[@]}" | jq -R . | jq -s .)
+
     echo "Checking for vulnerabilities with CVSS score >= 4.0..."
-    HIGH_SEVERITY_COUNT=$(grype litellm:latest -o json | jq -r '.matches[] | select(.vulnerability.cvss[]?.metrics.baseScore >= 4.0) | .vulnerability.id' | wc -l)
+    echo "Allowlisted CVEs (ignored in threshold): ${ALLOWED_CVES[*]}"
+
+    HIGH_SEVERITY_COUNT=$(grype litellm:latest -o json | jq --argjson allow "$ALLOWED_IDS_JSON" -r '
+        .matches[]
+        | select(.vulnerability.cvss[]?.metrics.baseScore >= 4.0)
+        | select((.vulnerability.id as $id | $allow | index($id) | not))
+        | .vulnerability.id' | wc -l)
     
     if [ "$HIGH_SEVERITY_COUNT" -gt 0 ]; then
         echo "ERROR: Found $HIGH_SEVERITY_COUNT vulnerabilities with CVSS score >= 4.0 in litellm:latest"
         echo "Detailed vulnerability report:"
-        grype litellm:latest -o json | jq -r '
+        grype litellm:latest -o json | jq --argjson allow "$ALLOWED_IDS_JSON" -r '
         ["Package", "Version", "Vulnerability ID", "CVSS Score", "Severity", "Fix Version", "Description"],
-        (.matches[] | select(.vulnerability.cvss[]?.metrics.baseScore >= 4.0) |
-        [.artifact.name, .artifact.version, .vulnerability.id, .vulnerability.cvss[0].metrics.baseScore, .vulnerability.severity, (.vulnerability.fix.versions[0] // "No fix available"), .vulnerability.description]) |
-        @tsv' | column -t -s $'\t'
+        (.matches[]
+          | select(.vulnerability.cvss[]?.metrics.baseScore >= 4.0)
+          | select((.vulnerability.id as $id | $allow | index($id) | not))
+          | [.artifact.name, .artifact.version, .vulnerability.id, .vulnerability.cvss[0].metrics.baseScore, .vulnerability.severity, (.vulnerability.fix.versions[0] // "No fix available"), .vulnerability.description])
+        | @tsv' | column -t -s $'\t'
         exit 1
     else
         echo "No high-severity vulnerabilities (CVSS >= 4.0) found in litellm:latest"
diff --git a/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md b/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md
index b0a24ef28e..d47de5b087 100644
--- a/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md
+++ b/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md
@@ -26,7 +26,7 @@ git diff <previous_commit_hash> HEAD -- model_prices_and_context_window.json
 
 ### 2. Release Notes Structure
 
-Follow this exact structure based on recent stable releases (v1.76.3-stable, v1.77.2-stable):
+Follow this exact structure based on recent stable releases (v1.76.3-stable, v1.77.2-stable, v1.77.5-stable):
 
 ```markdown
 ---
@@ -41,7 +41,7 @@ hide_table_of_contents: false
 [Docker and pip installation tabs]
 
 ## Key Highlights
-[3-5 bullet points of major features]
+[3-5 bullet points of major features - prioritize MCP OAuth 2.0, scheduled key rotations, and major model updates]
 
 ## New Models / Updated Models
 #### New Model Support
@@ -65,26 +65,32 @@ hide_table_of_contents: false
 
 ## Management Endpoints / UI
 #### Features
-[UI and management features]
+[UI and management features - group by functionality like Proxy CLI Auth, Virtual Keys, Models + Endpoints]
 
 #### Bugs
 [Management-related bug fixes]
 
-## Logging / Guardrail Integrations
+## Logging / Guardrail / Prompt Management Integrations
 #### Features
 [Organized by integration provider with proper doc links]
 
 #### Guardrails
 [Guardrail-specific features and fixes]
 
-#### New Integration
-[Major new integrations]
+#### Prompt Management
+[Prompt management integrations like BitBucket]
+
+## Spend Tracking, Budgets and Rate Limiting
+[Cost tracking, service tier pricing, rate limiting improvements]
+
+## MCP Gateway
+[MCP-specific features, OAuth 2.0, configuration improvements]
 
 ## Performance / Loadbalancing / Reliability improvements
-[Infrastructure improvements]
+[Infrastructure improvements, memory fixes, performance optimizations]
 
-## General Proxy Improvements
-[Other proxy-related changes]
+## Documentation Updates
+[Documentation improvements, guides, corrections - separate section for visibility]
 
 ## New Contributors
 [List of first-time contributors]
@@ -101,6 +107,11 @@ hide_table_of_contents: false
 - CPU usage optimizations
 - Timeout controls
 - Worker configuration
+- Memory leak fixes
+- Cache performance improvements
+- Database connection management
+- Dependency management (fastuuid, etc.)
+- Configuration management
 
 **New Models/Updated Models:**
 - Extract from model_prices_and_context_window.json diff
@@ -132,20 +143,32 @@ hide_table_of_contents: false
 - Dashboard improvements
 - Team management
 - Key management
+- Proxy CLI authentication and improvements
+- Virtual key management and scheduled rotations
+- SSO configuration fixes
+- Admin settings updates
+- Management routes and endpoints
 
-**Logging / Guardrail Integrations:**
+**Logging / Guardrail / Prompt Management Integrations:**
 - **Structure:**
   - `#### Features` - organized by integration provider with proper doc links
   - `#### Guardrails` - guardrail-specific features and fixes
+  - `#### Prompt Management` - prompt management integrations
   - `#### New Integration` - major new integrations
 - **Integration Categories:**
   - **[DataDog](../../docs/proxy/logging#datadog)** - group all DataDog-related changes
   - **[Langfuse](../../docs/proxy/logging#langfuse)** - Langfuse-specific features
   - **[Prometheus](../../docs/proxy/logging#prometheus)** - monitoring improvements
   - **[PostHog](../../docs/observability/posthog)** - observability integration
+  - **[SQS](../../docs/proxy/logging#sqs)** - SQS logging features
+  - **[Opik](../../docs/proxy/logging#opik)** - Opik integration improvements
   - Other logging providers with proper doc links
+- **Guardrail Categories:**
+  - LakeraAI, Presidio, Noma, and other guardrail providers
+- **Prompt Management:**
+  - BitBucket, GitHub, and other prompt management integrations
 - Use bullet points under each provider for multiple features
-- Separate logging features from guardrails clearly
+- Separate logging features from guardrails and prompt management clearly
 
 ### 4. Documentation Linking Strategy
 
@@ -189,15 +212,26 @@ From git diff analysis, create tables like:
 - `[Perf]`, `Performance`, `RPS` → Performance Improvements
 - `[Bug]`, `[Bug Fix]`, `Fix` → Bug Fixes section
 - `[Feat]`, `[Feature]`, `Add support` → Features section
-- `[Docs]` → Documentation (usually exclude from main sections)
+- `[Docs]` → Documentation Updates section
 - Provider names (Gemini, OpenAI, etc.) → Group under provider
+- `MCP`, `oauth`, `Model Context Protocol` → MCP Gateway
+- `service_tier`, `priority`, `cost tracking` → Spend Tracking, Budgets and Rate Limiting
 
 **By PR Content Analysis:**
 - New model additions → New Models section
 - UI changes → Management Endpoints/UI
-- Logging/observability → Logging/Guardrail Integrations
-- Rate limiting/budgets → Performance/Reliability
-- Authentication → Management Endpoints
+- Logging/observability → Logging/Guardrail/Prompt Management Integrations
+- Rate limiting/budgets → Spend Tracking, Budgets and Rate Limiting
+- Authentication → Management Endpoints/UI
+- MCP-related changes → MCP Gateway
+- Documentation updates → Documentation Updates
+- Performance/memory fixes → Performance/Loadbalancing/Reliability improvements
+
+**Special Categorization Rules:**
+- **Service tier pricing** (OpenAI priority/flex) → Spend Tracking section (NOT provider features)
+- **Cost breakdown in logging** → Spend Tracking section
+- **MCP configuration/OAuth** → MCP Gateway (NOT General Proxy Improvements)
+- **All documentation PRs** → Documentation Updates section for visibility
 
 ### 7. Writing Style Guidelines
 
@@ -226,6 +260,18 @@ From git diff analysis, create tables like:
 - Ensure model pricing is accurate
 - Confirm provider names are consistent
 - Review for typos and formatting issues
+- **Count PRs by section** - Provide final count like:
+  ```
+  ## MM/DD/YYYY
+  * New Models / Updated Models: XX
+  * LLM API Endpoints: XX
+  * Management Endpoints / UI: XX
+  * Logging / Guardrail / Prompt Management Integrations: XX
+  * Spend Tracking, Budgets and Rate Limiting: XX
+  * MCP Gateway: XX
+  * Performance / Loadbalancing / Reliability improvements: XX
+  * Documentation Updates: XX
+  ```
 
 ### 9. Common Patterns to Follow
 
@@ -295,6 +341,40 @@ This release has a known issue...
 - Complex configuration options
 - Migration requirements
 
+### 11. New Sections and Categories (Added in v1.77.5)
+
+**MCP Gateway Section:**
+- All MCP-related changes go here (not in General Proxy Improvements)
+- OAuth 2.0 flow improvements
+- MCP configuration and tools
+- Server management features
+
+**Spend Tracking, Budgets and Rate Limiting Section:**
+- Service tier pricing (OpenAI priority/flex pricing)
+- Cost tracking and breakdown features
+- Rate limiting improvements (Parallel Request Limiter v3)
+- Priority reservation fixes
+- Metadata handling for rate limiting
+
+**Documentation Updates Section:**
+- Create separate section for all documentation improvements
+- Include provider documentation fixes
+- Model reference updates
+- New guides and tutorials
+- Documentation corrections and clarifications
+- This gives documentation changes proper visibility
+
+**Management Endpoints / UI Grouping:**
+- Group related features under sub-categories:
+  - **Proxy CLI Auth** - CLI authentication improvements
+  - **Virtual Keys** - Key rotation and management
+  - **Models + Endpoints** - Provider and endpoint management
+
+**Logging Section Expansion:**
+- Rename to "Logging / Guardrail / Prompt Management Integrations"
+- Add **Prompt Management** subsection for BitBucket, GitHub integrations
+- Keep guardrails separate from logging features
+
 ## Example Command Workflow
 
 ```bash
diff --git a/docs/my-website/img/release_notes/perf_imp.png b/docs/my-website/img/release_notes/perf_imp.png
new file mode 100644
index 0000000000..9fef6a6b2d
Binary files /dev/null and b/docs/my-website/img/release_notes/perf_imp.png differ
diff --git a/docs/my-website/img/release_notes/quota.png b/docs/my-website/img/release_notes/quota.png
new file mode 100644
index 0000000000..f8d15747f8
Binary files /dev/null and b/docs/my-website/img/release_notes/quota.png differ
diff --git a/docs/my-website/release_notes/v1.77.3-stable/index.md b/docs/my-website/release_notes/v1.77.3-stable/index.md
index adea34f978..c7c17e5bae 100644
--- a/docs/my-website/release_notes/v1.77.3-stable/index.md
+++ b/docs/my-website/release_notes/v1.77.3-stable/index.md
@@ -1,5 +1,5 @@
 ---
-title: "[Preview] v1.77.3-stable - Priority Based Rate Limiting"
+title: "v1.77.3-stable - Priority Based Rate Limiting"
 slug: "v1-77-3"
 date: 2025-09-21T10:00:00
 authors:
@@ -28,7 +28,7 @@ import TabItem from '@theme/TabItem';
 docker run \
 -e STORE_MODEL_IN_DB=True \
 -p 4000:4000 \
-ghcr.io/berriai/litellm:main-v1.77.3.rc.1
+ghcr.io/berriai/litellm:v1.77.3-stable
 ```
 
 </TabItem>
@@ -51,11 +51,27 @@ pip install litellm==1.77.3
 
 ## Priority Quota Reservation
 
+This release adds support for priority quota reservation. This allows Proxy Admins to reserve specific percentages of model capacity for different use cases. 
+ 
+This is great for use cases where you want to ensure your realtime use cases must always get priority responses and background development jobs can take longer. 
+
+<Image img={require('../../img/release_notes/quota.png')}  style={{ width: '800px', height: 'auto' }} />
+
+<br/>
+
 This release adds support for priority quota reservation. This allows **Proxy Admins** to reserve TPM/RPM capacity for keys based on metadata priority levels, ensuring critical production workloads get guaranteed access regardless of development traffic volume.
 
 Get started [here](../../docs/proxy/dynamic_rate_limit#priority-quota-reservation)
 
-<iframe width="700" height="500" src="https://www.loom.com/embed/1b54b93139ee415d959402cc0629f3f7" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>
+## +550 RPS Performance Improvements
+
+<Image img={require('../../img/release_notes/perf_imp.png')}  style={{ width: '800px', height: 'auto' }} />
+
+<br/>
+
+This release delivers significant RPS improvements through targeted optimizations. 
+ 
+We've achieved a +500 RPS boost by fixing cache type inconsistencies that were causing frequent cache misses, plus an additional +50 RPS by removing unnecessary coroutine checks from the hot path. 
 
 
 ## New Models / Updated Models
diff --git a/docs/my-website/release_notes/v1.77.5-stable/index.md b/docs/my-website/release_notes/v1.77.5-stable/index.md
new file mode 100644
index 0000000000..50ddfe7794
--- /dev/null
+++ b/docs/my-website/release_notes/v1.77.5-stable/index.md
@@ -0,0 +1,285 @@
+---
+title: "[Preview] v1.77.5-stable - MCP OAuth 2.0 Support"
+slug: "v1-77-5"
+date: 2025-09-29T10:00:00
+authors:
+  - name: Krrish Dholakia
+    title: CEO, LiteLLM
+    url: https://www.linkedin.com/in/krish-d/
+    image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
+  - name: Ishaan Jaff
+    title: CTO, LiteLLM
+    url: https://www.linkedin.com/in/reffajnaahsi/
+    image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+
+hide_table_of_contents: false
+---
+
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+## Deploy this version
+
+<Tabs>
+<TabItem value="docker" label="Docker">
+
+``` showLineNumbers title="docker run litellm"
+```
+
+</TabItem>
+
+<TabItem value="pip" label="Pip">
+
+``` showLineNumbers title="pip install litellm"
+```
+
+</TabItem>
+</Tabs>
+
+---
+
+## Key Highlights
+
+- **MCP OAuth 2.0 Support** - Enhanced authentication for Model Context Protocol integrations
+- **Scheduled Key Rotations** - Automated key rotation capabilities for enhanced security
+- **New Gemini 2.5 Flash & Flash-lite Models** - Latest September 2025 preview models with improved pricing and features
+- **Performance Improvements** - Critical InMemoryCache unbounded growth resolution
+
+## New Models / Updated Models
+
+#### New Model Support
+
+| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features |
+| -------- | ----- | -------------- | ------------------- | -------------------- | -------- |
+| Gemini | `gemini-2.5-flash-preview-09-2025` | 1M | $0.30 | $2.50 | Chat, reasoning, vision, audio |
+| Gemini | `gemini-2.5-flash-lite-preview-09-2025` | 1M | $0.10 | $0.40 | Chat, reasoning, vision, audio |
+| Gemini | `gemini-flash-latest` | 1M | $0.30 | $2.50 | Chat, reasoning, vision, audio |
+| Gemini | `gemini-flash-lite-latest` | 1M | $0.10 | $0.40 | Chat, reasoning, vision, audio |
+| DeepSeek | `deepseek-chat` | 131K | $0.60 | $1.70 | Chat, function calling, caching |
+| DeepSeek | `deepseek-reasoner` | 131K | $0.60 | $1.70 | Chat, reasoning |
+| Bedrock | `deepseek.v3-v1:0` | 164K | $0.58 | $1.68 | Chat, reasoning, function calling |
+| Azure | `azure/gpt-5-codex` | 272K | $1.25 | $10.00 | Responses API, reasoning, vision |
+| OpenAI | `gpt-5-codex` | 272K | $1.25 | $10.00 | Responses API, reasoning, vision |
+| SambaNova | `sambanova/DeepSeek-V3.1` | 33K | $3.00 | $4.50 | Chat, reasoning, function calling |
+| SambaNova | `sambanova/gpt-oss-120b` | 131K | $3.00 | $4.50 | Chat, reasoning, function calling |
+| Bedrock | `qwen.qwen3-coder-480b-a35b-v1:0` | 262K | $0.22 | $1.80 | Chat, reasoning, function calling |
+| Bedrock | `qwen.qwen3-235b-a22b-2507-v1:0` | 262K | $0.22 | $0.88 | Chat, reasoning, function calling |
+| Bedrock | `qwen.qwen3-coder-30b-a3b-v1:0` | 262K | $0.15 | $0.60 | Chat, reasoning, function calling |
+| Bedrock | `qwen.qwen3-32b-v1:0` | 131K | $0.15 | $0.60 | Chat, reasoning, function calling |
+| Vertex AI | `vertex_ai/qwen/qwen3-next-80b-a3b-instruct-maas` | 262K | $0.15 | $1.20 | Chat, function calling |
+| Vertex AI | `vertex_ai/qwen/qwen3-next-80b-a3b-thinking-maas` | 262K | $0.15 | $1.20 | Chat, function calling |
+| Vertex AI | `vertex_ai/deepseek-ai/deepseek-v3.1-maas` | 164K | $1.35 | $5.40 | Chat, reasoning, function calling |
+| OpenRouter | `openrouter/x-ai/grok-4-fast:free` | 2M | $0.00 | $0.00 | Chat, reasoning, function calling |
+| XAI | `xai/grok-4-fast-reasoning` | 2M | $0.20 | $0.50 | Chat, reasoning, function calling |
+| XAI | `xai/grok-4-fast-non-reasoning` | 2M | $0.20 | $0.50 | Chat, function calling |
+
+#### Features
+
+- **[Gemini](../../docs/providers/gemini)**
+    - Added Gemini 2.5 Flash and Flash-lite preview models (September 2025 release) with improved pricing - [PR #14948](https://github.com/BerriAI/litellm/pull/14948)
+    - Added new Anthropic web fetch tool support - [PR #14951](https://github.com/BerriAI/litellm/pull/14951)
+- **[XAI](../../docs/providers/xai)**
+    - Add xai/grok-4-fast models - [PR #14833](https://github.com/BerriAI/litellm/pull/14833)
+- **[Anthropic](../../docs/providers/anthropic)**
+    - Updated Claude Sonnet 4 configs to reflect million-token context window pricing - [PR #14639](https://github.com/BerriAI/litellm/pull/14639)
+    - Added supported text field to anthropic citation response - [PR #14164](https://github.com/BerriAI/litellm/pull/14164)
+- **[Bedrock](../../docs/providers/bedrock)**
+    - Added support for Qwen models family & Deepseek 3.1 to Amazon Bedrock - [PR #14845](https://github.com/BerriAI/litellm/pull/14845)
+    - Support requestMetadata in Bedrock Converse API - [PR #14570](https://github.com/BerriAI/litellm/pull/14570)
+- **[Vertex AI](../../docs/providers/vertex)**
+    - Added vertex_ai/qwen models and azure/gpt-5-codex - [PR #14844](https://github.com/BerriAI/litellm/pull/14844)
+    - Update vertex ai qwen model pricing - [PR #14828](https://github.com/BerriAI/litellm/pull/14828)
+    - Vertex AI Context Caching: use Vertex ai API v1 instead of v1beta1 and accept 'cachedContent' param - [PR #14831](https://github.com/BerriAI/litellm/pull/14831)
+- **[SambaNova](../../docs/providers/sambanova)**
+    - Add sambanova deepseek v3.1 and gpt-oss-120b - [PR #14866](https://github.com/BerriAI/litellm/pull/14866)
+- **[OpenAI](../../docs/providers/openai)**
+    - Fix inconsistent token configs for gpt-5 models - [PR #14942](https://github.com/BerriAI/litellm/pull/14942)
+    - GPT-3.5-Turbo price updated - [PR #14858](https://github.com/BerriAI/litellm/pull/14858)
+- **[OpenRouter](../../docs/providers/openrouter)**
+    - Add gpt-5 and gpt-5-codex to OpenRouter cost map - [PR #14879](https://github.com/BerriAI/litellm/pull/14879)
+- **[VLLM](../../docs/providers/vllm)**
+    - Fix vllm passthrough - [PR #14778](https://github.com/BerriAI/litellm/pull/14778)
+- **[Flux](../../docs/image_generation)**
+    - Support flux image edit - [PR #14790](https://github.com/BerriAI/litellm/pull/14790)
+
+### Bug Fixes
+
+- **[Anthropic](../../docs/providers/anthropic)**
+    - Fix: Support claude code auth via subscription (anthropic) - [PR #14821](https://github.com/BerriAI/litellm/pull/14821)
+    - Fix Anthropic streaming IDs - [PR #14965](https://github.com/BerriAI/litellm/pull/14965)
+    - Revert incorrect changes to sonnet-4 max output tokens - [PR #14933](https://github.com/BerriAI/litellm/pull/14933)
+- **[OpenAI](../../docs/providers/openai)**
+    - Fix a bug where openai image edit silently ignores multiple images - [PR #14893](https://github.com/BerriAI/litellm/pull/14893)
+- **[VLLM](../../docs/providers/vllm)**
+    - Fix: vLLM provider's rerank endpoint from /v1/rerank to /rerank - [PR #14938](https://github.com/BerriAI/litellm/pull/14938)
+
+#### New Provider Support
+
+- **[W&B Inference](../../docs/providers/wandb)**
+    - Add W&B Inference to LiteLLM - [PR #14416](https://github.com/BerriAI/litellm/pull/14416)
+
+---
+
+## LLM API Endpoints
+
+#### Features
+
+- **General**
+    - Add SDK support for additional headers - [PR #14761](https://github.com/BerriAI/litellm/pull/14761)
+    - Add shared_session parameter for aiohttp ClientSession reuse - [PR #14721](https://github.com/BerriAI/litellm/pull/14721)
+
+#### Bugs
+
+- **General**
+    - Fix: Streaming tool call index assignment for multiple tool calls - [PR #14587](https://github.com/BerriAI/litellm/pull/14587)
+    - Fix load credentials in token counter proxy - [PR #14808](https://github.com/BerriAI/litellm/pull/14808)
+
+---
+
+## Management Endpoints / UI
+
+#### Features
+
+- **Proxy CLI Auth** 
+    - Allow re-using cli auth token - [PR #14780](https://github.com/BerriAI/litellm/pull/14780)
+    - Create a python method to login using litellm proxy - [PR #14782](https://github.com/BerriAI/litellm/pull/14782)
+    - Fixes for LiteLLM Proxy CLI to Auth to Gateway - [PR #14836](https://github.com/BerriAI/litellm/pull/14836)
+    
+**Virtual Keys**    
+    - Initial support for scheduled key rotations - [PR #14877](https://github.com/BerriAI/litellm/pull/14877)
+    - Allow scheduling key rotations when creating virtual keys - [PR #14960](https://github.com/BerriAI/litellm/pull/14960)
+
+**Models + Endpoints**
+    - Fix: added Oracle to provider's list - [PR #14835](https://github.com/BerriAI/litellm/pull/14835)
+
+
+#### Bugs
+
+- **SSO** - Fix: SSO "Clear" button writes empty values instead of removing SSO config - [PR #14826](https://github.com/BerriAI/litellm/pull/14826)
+- **Admin Settings** - Remove useful links from admin settings - [PR #14918](https://github.com/BerriAI/litellm/pull/14918)
+- **Management Routes** - Add /user/list to management routes - [PR #14868](https://github.com/BerriAI/litellm/pull/14868)
+---
+
+## Logging / Guardrail / Prompt Management Integrations
+
+#### Features
+
+- **[DataDog](../../docs/proxy/logging#datadog)**
+    - Logging - `datadog` callback Log message content w/o sending to datadog - [PR #14909](https://github.com/BerriAI/litellm/pull/14909)
+- **[Langfuse](../../docs/proxy/logging#langfuse)**
+    - Adding langfuse usage details for cached tokens - [PR #10955](https://github.com/BerriAI/litellm/pull/10955)
+- **[Opik](../../docs/proxy/logging#opik)**
+    - Improve opik integration code - [PR #14888](https://github.com/BerriAI/litellm/pull/14888)
+- **[SQS](../../docs/proxy/logging#sqs)**
+    - Error logging support for SQS Logger - [PR #14974](https://github.com/BerriAI/litellm/pull/14974)
+
+#### Guardrails
+
+- **LakeraAI v2 Guardrail** - Ensure exception is raised correctly - [PR #14867](https://github.com/BerriAI/litellm/pull/14867)
+- **Presidio Guardrail** - Support custom entity types in Presidio guardrail with Union[PiiEntityType, str] - [PR #14899](https://github.com/BerriAI/litellm/pull/14899)
+- **Noma Guardrail** - Add noma guardrail provider to ui - [PR #14415](https://github.com/BerriAI/litellm/pull/14415)
+
+#### Prompt Management
+
+- **BitBucket Integration** - Add BitBucket Integration for Prompt Management - [PR #14882](https://github.com/BerriAI/litellm/pull/14882)
+
+---
+
+## Spend Tracking, Budgets and Rate Limiting
+
+- **Service Tier Pricing** - Add service_tier based pricing support for openai (BOTH Service & Priority Support) - [PR #14796](https://github.com/BerriAI/litellm/pull/14796)
+- **Cost Tracking** - Show input, output, tool call cost breakdown in StandardLoggingPayload - [PR #14921](https://github.com/BerriAI/litellm/pull/14921)
+- **Parallel Request Limiter v3** 
+    - Ensure Lua scripts can execute on redis cluster - [PR #14968](https://github.com/BerriAI/litellm/pull/14968)
+    - Fix: get metadata info from both metadata and litellm_metadata fields - [PR #14783](https://github.com/BerriAI/litellm/pull/14783)
+- **Priority Reservation** - Fix: Priority Reservation: keys without priority metadata receive higher priority than keys with explicit priority configurations - [PR #14832](https://github.com/BerriAI/litellm/pull/14832)
+
+---
+
+## MCP Gateway
+
+- **MCP Configuration** - Enable custom fields in mcp_info configuration - [PR #14794](https://github.com/BerriAI/litellm/pull/14794)
+- **MCP Tools** - Remove server_name prefix from list_tools - [PR #14720](https://github.com/BerriAI/litellm/pull/14720)
+- **OAuth Flow** - Initial commit for v2 oauth flow - [PR #14964](https://github.com/BerriAI/litellm/pull/14964)
+
+---
+
+## Performance / Loadbalancing / Reliability improvements
+
+- **Memory Leak Fix** - Fix InMemoryCache unbounded growth when TTLs are set - [PR #14869](https://github.com/BerriAI/litellm/pull/14869)
+- **Cache Performance** - Fix: cache root cause - [PR #14827](https://github.com/BerriAI/litellm/pull/14827)
+- **Concurrency Fix** - Fix concurrency/scaling when many Python threads do streaming using *sync* completions - [PR #14816](https://github.com/BerriAI/litellm/pull/14816)
+- **Performance Optimization** - Fix: reduce get_deployment cost to O(1) - [PR #14967](https://github.com/BerriAI/litellm/pull/14967)
+- **Performance Optimization** - Fix: remove slow string operation - [PR #14955](https://github.com/BerriAI/litellm/pull/14955)
+- **DB Connection Management** - Fix: DB connection state retries - [PR #14925](https://github.com/BerriAI/litellm/pull/14925)
+
+
+
+---
+
+## Documentation Updates
+
+- **Provider Documentation** - Fix docs for provider_specific_params.md - [PR #14787](https://github.com/BerriAI/litellm/pull/14787)
+- **Model References** - Update model references from gemini-pro to gemini-2.5-pro - [PR #14775](https://github.com/BerriAI/litellm/pull/14775)
+- **Letta Guide** - Add Letta Guide documentation - [PR #14798](https://github.com/BerriAI/litellm/pull/14798)
+- **README** - Make the README document clearer - [PR #14860](https://github.com/BerriAI/litellm/pull/14860)
+- **Session Management** - Update docs for session management availability - [PR #14914](https://github.com/BerriAI/litellm/pull/14914)
+- **Cost Documentation** - Add documentation for additional cost-related keys in custom pricing - [PR #14949](https://github.com/BerriAI/litellm/pull/14949)
+- **Azure Passthrough** - Add azure passthrough documentation - [PR #14958](https://github.com/BerriAI/litellm/pull/14958)
+- **General Documentation** - Doc updates sept 2025 - [PR #14769](https://github.com/BerriAI/litellm/pull/14769)
+    - Clarified bridging between endpoints and mode in docs.
+    - Added Vertex AI Gemini API configuration as an alternative in relevant guides.
+    Linked AWS authentication info in the Bedrock guardrails documentation.
+    - Added Cancel Response API usage with code snippets
+    - Clarified that SSO (Single Sign-On) is free for up to 5 users:
+    - Alphabetized sidebar, leaving quick start / intros at top of categories
+    - Documented max_connections under cache_params.
+    - Clarified IAM AssumeRole Policy requirements.
+    - Added transform utilities example to Getting Started (showing request transformation).
+    - Added references to models.litellm.ai as the full models list in various docs.
+    - Added a code snippet for async_post_call_success_hook.
+    - Removed broken links to callbacks management guide. - Reformatted and linked cookbooks + other relevant docs
+- **Documentation Corrections** - Corrected docs updates sept 2025 - [PR #14916](https://github.com/BerriAI/litellm/pull/14916)
+
+---
+
+## New Contributors
+
+* @uzaxirr made their first contribution in [PR #14761](https://github.com/BerriAI/litellm/pull/14761)
+* @xprilion made their first contribution in [PR #14416](https://github.com/BerriAI/litellm/pull/14416)
+* @CH-GAGANRAJ made their first contribution in [PR #14779](https://github.com/BerriAI/litellm/pull/14779)
+* @otaviofbrito made their first contribution in [PR #14778](https://github.com/BerriAI/litellm/pull/14778)
+* @danielmklein made their first contribution in [PR #14639](https://github.com/BerriAI/litellm/pull/14639)
+* @Jetemple made their first contribution in [PR #14826](https://github.com/BerriAI/litellm/pull/14826)
+* @akshoop made their first contribution in [PR #14818](https://github.com/BerriAI/litellm/pull/14818)
+* @hazyone made their first contribution in [PR #14821](https://github.com/BerriAI/litellm/pull/14821)
+* @leventov made their first contribution in [PR #14816](https://github.com/BerriAI/litellm/pull/14816)
+* @fabriciojoc made their first contribution in [PR #10955](https://github.com/BerriAI/litellm/pull/10955)
+* @onlylonly made their first contribution in [PR #14845](https://github.com/BerriAI/litellm/pull/14845)
+* @Copilot made their first contribution in [PR #14869](https://github.com/BerriAI/litellm/pull/14869)
+* @arsh72 made their first contribution in [PR #14899](https://github.com/BerriAI/litellm/pull/14899)
+* @berri-teddy made their first contribution in [PR #14914](https://github.com/BerriAI/litellm/pull/14914)
+* @vpbill made their first contribution in [PR #14415](https://github.com/BerriAI/litellm/pull/14415)
+* @kgritesh made their first contribution in [PR #14893](https://github.com/BerriAI/litellm/pull/14893)
+* @oytunkutrup1 made their first contribution in [PR #14858](https://github.com/BerriAI/litellm/pull/14858)
+* @nherment made their first contribution in [PR #14933](https://github.com/BerriAI/litellm/pull/14933)
+* @deepanshululla made their first contribution in [PR #14974](https://github.com/BerriAI/litellm/pull/14974)
+* @TeddyAmkie made their first contribution in [PR #14758](https://github.com/BerriAI/litellm/pull/14758)
+* @SmartManoj made their first contribution in [PR #14775](https://github.com/BerriAI/litellm/pull/14775)
+* @uc4w6c made their first contribution in [PR #14720](https://github.com/BerriAI/litellm/pull/14720)
+* @luizrennocosta made their first contribution in [PR #14783](https://github.com/BerriAI/litellm/pull/14783)
+* @AlexsanderHamir made their first contribution in [PR #14827](https://github.com/BerriAI/litellm/pull/14827)
+* @dharamendrak made their first contribution in [PR #14721](https://github.com/BerriAI/litellm/pull/14721)
+* @TomeHirata made their first contribution in [PR #14164](https://github.com/BerriAI/litellm/pull/14164)
+* @mrFranklin made their first contribution in [PR #14860](https://github.com/BerriAI/litellm/pull/14860)
+* @luisfucros made their first contribution in [PR #14866](https://github.com/BerriAI/litellm/pull/14866)
+* @huangyafei made their first contribution in [PR #14879](https://github.com/BerriAI/litellm/pull/14879)
+* @thiswillbeyourgithub made their first contribution in [PR #14949](https://github.com/BerriAI/litellm/pull/14949)
+* @Maximgitman made their first contribution in [PR #14965](https://github.com/BerriAI/litellm/pull/14965)
+* @subnet-dev made their first contribution in [PR #14938](https://github.com/BerriAI/litellm/pull/14938)
+* @22mSqRi made their first contribution in [PR #14972](https://github.com/BerriAI/litellm/pull/14972)
+
+---
+
+## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.77.3.rc.1...v1.77.5.rc.1)**
diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py
index 4451d76bed..d3b0aefb86 100644
--- a/enterprise/litellm_enterprise/integrations/prometheus.py
+++ b/enterprise/litellm_enterprise/integrations/prometheus.py
@@ -2262,9 +2262,12 @@ def get_custom_labels_from_metadata(metadata: dict) -> Dict[str, str]:
 
         keys_parts = key.split(".")
         # Traverse through the dictionary using the parts
-        value = metadata
+        value: Any = metadata
         for part in keys_parts:
-            value = value.get(part, None)  # Get the value, return None if not found
+            if isinstance(value, dict):
+                value = value.get(part, None)  # Get the value, return None if not found
+            else:
+                value = None
             if value is None:
                 break
 
diff --git a/litellm/__init__.py b/litellm/__init__.py
index 6df9d0780a..02bb773d26 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -377,7 +377,9 @@ public_model_groups: Optional[List[str]] = None
 public_model_groups_links: Dict[str, str] = {}
 #### REQUEST PRIORITIZATION ######
 priority_reservation: Optional[Dict[str, float]] = None
-priority_reservation_settings: "PriorityReservationSettings" = PriorityReservationSettings()
+priority_reservation_settings: "PriorityReservationSettings" = (
+    PriorityReservationSettings()
+)
 
 
 ######## Networking Settings ########
@@ -443,7 +445,7 @@ def identify(event_details):
 ####### ADDITIONAL PARAMS ################### configurable params if you use proxy models like Helicone, map spend to org id, etc.
 api_base: Optional[str] = None
 headers = None
-api_version = None
+api_version: Optional[str] = None
 organization = None
 project = None
 config_path = None
@@ -494,7 +496,7 @@ azure_ai_models: Set = set()
 jina_ai_models: Set = set()
 voyage_models: Set = set()
 infinity_models: Set = set()
-heroku_models: Set = set() 
+heroku_models: Set = set()
 databricks_models: Set = set()
 cloudflare_models: Set = set()
 codestral_models: Set = set()
@@ -1357,6 +1359,7 @@ from .passthrough import allm_passthrough_route, llm_passthrough_route
 ### GLOBAL CONFIG ###
 global_bitbucket_config: Optional[Dict[str, Any]] = None
 
+
 def set_global_bitbucket_config(config: Dict[str, Any]) -> None:
     """Set global BitBucket configuration for prompt management."""
     global global_bitbucket_config
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index ea45f45d1c..bbadc9c818 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -301,9 +301,9 @@ class Logging(LiteLLMLoggingBaseClass):
         self.litellm_trace_id: str = litellm_trace_id or str(uuid.uuid4())
         self.function_id = function_id
         self.streaming_chunks: List[Any] = []  # for generating complete stream response
-        self.sync_streaming_chunks: List[
-            Any
-        ] = []  # for generating complete stream response
+        self.sync_streaming_chunks: List[Any] = (
+            []
+        )  # for generating complete stream response
         self.log_raw_request_response = log_raw_request_response
 
         # Initialize dynamic callbacks
@@ -344,7 +344,7 @@ class Logging(LiteLLMLoggingBaseClass):
             litellm_params = scrub_sensitive_keys_in_metadata(litellm_params)
 
         self.litellm_params = litellm_params
-        
+
         # Initialize cost breakdown field
         self.cost_breakdown: Optional[CostBreakdown] = None
 
@@ -676,9 +676,9 @@ class Logging(LiteLLMLoggingBaseClass):
         if anthropic_cache_control_logger := AnthropicCacheControlHook.get_custom_logger_for_anthropic_cache_control_hook(
             non_default_params
         ):
-            self.model_call_details[
-                "prompt_integration"
-            ] = anthropic_cache_control_logger.__class__.__name__
+            self.model_call_details["prompt_integration"] = (
+                anthropic_cache_control_logger.__class__.__name__
+            )
             return anthropic_cache_control_logger
 
         #########################################################
@@ -690,9 +690,9 @@ class Logging(LiteLLMLoggingBaseClass):
                 internal_usage_cache=None,
                 llm_router=None,
             )
-            self.model_call_details[
-                "prompt_integration"
-            ] = vector_store_custom_logger.__class__.__name__
+            self.model_call_details["prompt_integration"] = (
+                vector_store_custom_logger.__class__.__name__
+            )
             return vector_store_custom_logger
 
         return None
@@ -744,9 +744,9 @@ class Logging(LiteLLMLoggingBaseClass):
             model
         ):  # if model name was changes pre-call, overwrite the initial model call name with the new one
             self.model_call_details["model"] = model
-        self.model_call_details["litellm_params"][
-            "api_base"
-        ] = self._get_masked_api_base(additional_args.get("api_base", ""))
+        self.model_call_details["litellm_params"]["api_base"] = (
+            self._get_masked_api_base(additional_args.get("api_base", ""))
+        )
 
     def pre_call(self, input, api_key, model=None, additional_args={}):  # noqa: PLR0915
         # Log the exact input to the LLM API
@@ -775,10 +775,10 @@ class Logging(LiteLLMLoggingBaseClass):
                 try:
                     # [Non-blocking Extra Debug Information in metadata]
                     if turn_off_message_logging is True:
-                        _metadata[
-                            "raw_request"
-                        ] = "redacted by litellm. \
+                        _metadata["raw_request"] = (
+                            "redacted by litellm. \
                             'litellm.turn_off_message_logging=True'"
+                        )
                     else:
                         curl_command = self._get_request_curl_command(
                             api_base=additional_args.get("api_base", ""),
@@ -789,32 +789,32 @@ class Logging(LiteLLMLoggingBaseClass):
 
                         _metadata["raw_request"] = str(curl_command)
                         # split up, so it's easier to parse in the UI
-                        self.model_call_details[
-                            "raw_request_typed_dict"
-                        ] = RawRequestTypedDict(
-                            raw_request_api_base=str(
-                                additional_args.get("api_base") or ""
-                            ),
-                            raw_request_body=self._get_raw_request_body(
-                                additional_args.get("complete_input_dict", {})
-                            ),
-                            raw_request_headers=self._get_masked_headers(
-                                additional_args.get("headers", {}) or {},
-                                ignore_sensitive_headers=True,
-                            ),
-                            error=None,
+                        self.model_call_details["raw_request_typed_dict"] = (
+                            RawRequestTypedDict(
+                                raw_request_api_base=str(
+                                    additional_args.get("api_base") or ""
+                                ),
+                                raw_request_body=self._get_raw_request_body(
+                                    additional_args.get("complete_input_dict", {})
+                                ),
+                                raw_request_headers=self._get_masked_headers(
+                                    additional_args.get("headers", {}) or {},
+                                    ignore_sensitive_headers=True,
+                                ),
+                                error=None,
+                            )
                         )
                 except Exception as e:
-                    self.model_call_details[
-                        "raw_request_typed_dict"
-                    ] = RawRequestTypedDict(
-                        error=str(e),
+                    self.model_call_details["raw_request_typed_dict"] = (
+                        RawRequestTypedDict(
+                            error=str(e),
+                        )
                     )
-                    _metadata[
-                        "raw_request"
-                    ] = "Unable to Log \
+                    _metadata["raw_request"] = (
+                        "Unable to Log \
                         raw request: {}".format(
-                        str(e)
+                            str(e)
+                        )
                     )
             if getattr(self, "logger_fn", None) and callable(self.logger_fn):
                 try:
@@ -1115,13 +1115,13 @@ class Logging(LiteLLMLoggingBaseClass):
         for callback in callbacks:
             try:
                 if isinstance(callback, CustomLogger):
-                    response: Optional[
-                        MCPPostCallResponseObject
-                    ] = await callback.async_post_mcp_tool_call_hook(
-                        kwargs=kwargs,
-                        response_obj=post_mcp_tool_call_response_obj,
-                        start_time=start_time,
-                        end_time=end_time,
+                    response: Optional[MCPPostCallResponseObject] = (
+                        await callback.async_post_mcp_tool_call_hook(
+                            kwargs=kwargs,
+                            response_obj=post_mcp_tool_call_response_obj,
+                            start_time=start_time,
+                            end_time=end_time,
+                        )
                     )
                     ######################################################################
                     # if any of the callbacks modify the response, use the modified response
@@ -1168,19 +1168,19 @@ class Logging(LiteLLMLoggingBaseClass):
     ) -> None:
         """
         Helper method to store cost breakdown in the logging object.
-        
+
         Args:
             input_cost: Cost of input/prompt tokens
-            output_cost: Cost of output/completion tokens 
+            output_cost: Cost of output/completion tokens
             cost_for_built_in_tools_cost_usd_dollar: Cost of built-in tools
             total_cost: Total cost of request
         """
-        
+
         self.cost_breakdown = CostBreakdown(
             input_cost=input_cost,
             output_cost=output_cost,
             total_cost=total_cost,
-            tool_usage_cost=cost_for_built_in_tools_cost_usd_dollar
+            tool_usage_cost=cost_for_built_in_tools_cost_usd_dollar,
         )
         verbose_logger.debug(
             f"Cost breakdown set - input: {input_cost}, output: {output_cost}, cost_for_built_in_tools_cost_usd_dollar: {cost_for_built_in_tools_cost_usd_dollar}, total: {total_cost}"
@@ -1259,9 +1259,11 @@ class Logging(LiteLLMLoggingBaseClass):
                 "standard_built_in_tools_params": self.standard_built_in_tools_params,
                 "router_model_id": router_model_id,
                 "litellm_logging_obj": self,
-                "service_tier": self.optional_params.get("service_tier")
-                if self.optional_params
-                else None,
+                "service_tier": (
+                    self.optional_params.get("service_tier")
+                    if self.optional_params
+                    else None
+                ),
             }
         except Exception as e:  # error creating kwargs for cost calculation
             debug_info = StandardLoggingModelCostFailureDebugInformation(
@@ -1271,9 +1273,9 @@ class Logging(LiteLLMLoggingBaseClass):
             verbose_logger.debug(
                 f"response_cost_failure_debug_information: {debug_info}"
             )
-            self.model_call_details[
-                "response_cost_failure_debug_information"
-            ] = debug_info
+            self.model_call_details["response_cost_failure_debug_information"] = (
+                debug_info
+            )
             return None
 
         try:
@@ -1298,9 +1300,9 @@ class Logging(LiteLLMLoggingBaseClass):
             verbose_logger.debug(
                 f"response_cost_failure_debug_information: {debug_info}"
             )
-            self.model_call_details[
-                "response_cost_failure_debug_information"
-            ] = debug_info
+            self.model_call_details["response_cost_failure_debug_information"] = (
+                debug_info
+            )
 
         return None
 
@@ -1444,9 +1446,9 @@ class Logging(LiteLLMLoggingBaseClass):
                 end_time = datetime.datetime.now()
             if self.completion_start_time is None:
                 self.completion_start_time = end_time
-                self.model_call_details[
-                    "completion_start_time"
-                ] = self.completion_start_time
+                self.model_call_details["completion_start_time"] = (
+                    self.completion_start_time
+                )
             self.model_call_details["log_event_type"] = "successful_api_call"
             self.model_call_details["end_time"] = end_time
             self.model_call_details["cache_hit"] = cache_hit
@@ -1499,39 +1501,39 @@ class Logging(LiteLLMLoggingBaseClass):
                             "response_cost"
                         ]
                     else:
-                        self.model_call_details[
-                            "response_cost"
-                        ] = self._response_cost_calculator(result=logging_result)
+                        self.model_call_details["response_cost"] = (
+                            self._response_cost_calculator(result=logging_result)
+                        )
                     ## STANDARDIZED LOGGING PAYLOAD
 
-                    self.model_call_details[
-                        "standard_logging_object"
-                    ] = get_standard_logging_object_payload(
-                        kwargs=self.model_call_details,
-                        init_response_obj=logging_result,
-                        start_time=start_time,
-                        end_time=end_time,
-                        logging_obj=self,
-                        status="success",
-                        standard_built_in_tools_params=self.standard_built_in_tools_params,
+                    self.model_call_details["standard_logging_object"] = (
+                        get_standard_logging_object_payload(
+                            kwargs=self.model_call_details,
+                            init_response_obj=logging_result,
+                            start_time=start_time,
+                            end_time=end_time,
+                            logging_obj=self,
+                            status="success",
+                            standard_built_in_tools_params=self.standard_built_in_tools_params,
+                        )
                     )
                 elif isinstance(result, dict) or isinstance(result, list):
                     ## STANDARDIZED LOGGING PAYLOAD
-                    self.model_call_details[
-                        "standard_logging_object"
-                    ] = get_standard_logging_object_payload(
-                        kwargs=self.model_call_details,
-                        init_response_obj=result,
-                        start_time=start_time,
-                        end_time=end_time,
-                        logging_obj=self,
-                        status="success",
-                        standard_built_in_tools_params=self.standard_built_in_tools_params,
+                    self.model_call_details["standard_logging_object"] = (
+                        get_standard_logging_object_payload(
+                            kwargs=self.model_call_details,
+                            init_response_obj=result,
+                            start_time=start_time,
+                            end_time=end_time,
+                            logging_obj=self,
+                            status="success",
+                            standard_built_in_tools_params=self.standard_built_in_tools_params,
+                        )
                     )
             elif standard_logging_object is not None:
-                self.model_call_details[
-                    "standard_logging_object"
-                ] = standard_logging_object
+                self.model_call_details["standard_logging_object"] = (
+                    standard_logging_object
+                )
             else:  # streaming chunks + image gen.
                 self.model_call_details["response_cost"] = None
 
@@ -1682,23 +1684,23 @@ class Logging(LiteLLMLoggingBaseClass):
                 verbose_logger.debug(
                     "Logging Details LiteLLM-Success Call streaming complete"
                 )
-                self.model_call_details[
-                    "complete_streaming_response"
-                ] = complete_streaming_response
-                self.model_call_details[
-                    "response_cost"
-                ] = self._response_cost_calculator(result=complete_streaming_response)
+                self.model_call_details["complete_streaming_response"] = (
+                    complete_streaming_response
+                )
+                self.model_call_details["response_cost"] = (
+                    self._response_cost_calculator(result=complete_streaming_response)
+                )
                 ## STANDARDIZED LOGGING PAYLOAD
-                self.model_call_details[
-                    "standard_logging_object"
-                ] = get_standard_logging_object_payload(
-                    kwargs=self.model_call_details,
-                    init_response_obj=complete_streaming_response,
-                    start_time=start_time,
-                    end_time=end_time,
-                    logging_obj=self,
-                    status="success",
-                    standard_built_in_tools_params=self.standard_built_in_tools_params,
+                self.model_call_details["standard_logging_object"] = (
+                    get_standard_logging_object_payload(
+                        kwargs=self.model_call_details,
+                        init_response_obj=complete_streaming_response,
+                        start_time=start_time,
+                        end_time=end_time,
+                        logging_obj=self,
+                        status="success",
+                        standard_built_in_tools_params=self.standard_built_in_tools_params,
+                    )
                 )
             callbacks = self.get_combined_callback_list(
                 dynamic_success_callbacks=self.dynamic_success_callbacks,
@@ -2026,10 +2028,10 @@ class Logging(LiteLLMLoggingBaseClass):
                             )
                         else:
                             if self.stream and complete_streaming_response:
-                                self.model_call_details[
-                                    "complete_response"
-                                ] = self.model_call_details.get(
-                                    "complete_streaming_response", {}
+                                self.model_call_details["complete_response"] = (
+                                    self.model_call_details.get(
+                                        "complete_streaming_response", {}
+                                    )
                                 )
                                 result = self.model_call_details["complete_response"]
                             openMeterLogger.log_success_event(
@@ -2068,10 +2070,10 @@ class Logging(LiteLLMLoggingBaseClass):
                             )
                         else:
                             if self.stream and complete_streaming_response:
-                                self.model_call_details[
-                                    "complete_response"
-                                ] = self.model_call_details.get(
-                                    "complete_streaming_response", {}
+                                self.model_call_details["complete_response"] = (
+                                    self.model_call_details.get(
+                                        "complete_streaming_response", {}
+                                    )
                                 )
                                 result = self.model_call_details["complete_response"]
 
@@ -2209,9 +2211,9 @@ class Logging(LiteLLMLoggingBaseClass):
         if complete_streaming_response is not None:
             print_verbose("Async success callbacks: Got a complete streaming response")
 
-            self.model_call_details[
-                "async_complete_streaming_response"
-            ] = complete_streaming_response
+            self.model_call_details["async_complete_streaming_response"] = (
+                complete_streaming_response
+            )
 
             try:
                 if self.model_call_details.get("cache_hit", False) is True:
@@ -2222,10 +2224,10 @@ class Logging(LiteLLMLoggingBaseClass):
                         model_call_details=self.model_call_details
                     )
                     # base_model defaults to None if not set on model_info
-                    self.model_call_details[
-                        "response_cost"
-                    ] = self._response_cost_calculator(
-                        result=complete_streaming_response
+                    self.model_call_details["response_cost"] = (
+                        self._response_cost_calculator(
+                            result=complete_streaming_response
+                        )
                     )
 
                 verbose_logger.debug(
@@ -2238,16 +2240,16 @@ class Logging(LiteLLMLoggingBaseClass):
                 self.model_call_details["response_cost"] = None
 
             ## STANDARDIZED LOGGING PAYLOAD
-            self.model_call_details[
-                "standard_logging_object"
-            ] = get_standard_logging_object_payload(
-                kwargs=self.model_call_details,
-                init_response_obj=complete_streaming_response,
-                start_time=start_time,
-                end_time=end_time,
-                logging_obj=self,
-                status="success",
-                standard_built_in_tools_params=self.standard_built_in_tools_params,
+            self.model_call_details["standard_logging_object"] = (
+                get_standard_logging_object_payload(
+                    kwargs=self.model_call_details,
+                    init_response_obj=complete_streaming_response,
+                    start_time=start_time,
+                    end_time=end_time,
+                    logging_obj=self,
+                    status="success",
+                    standard_built_in_tools_params=self.standard_built_in_tools_params,
+                )
             )
         callbacks = self.get_combined_callback_list(
             dynamic_success_callbacks=self.dynamic_async_success_callbacks,
@@ -2460,18 +2462,18 @@ class Logging(LiteLLMLoggingBaseClass):
 
         ## STANDARDIZED LOGGING PAYLOAD
 
-        self.model_call_details[
-            "standard_logging_object"
-        ] = get_standard_logging_object_payload(
-            kwargs=self.model_call_details,
-            init_response_obj={},
-            start_time=start_time,
-            end_time=end_time,
-            logging_obj=self,
-            status="failure",
-            error_str=str(exception),
-            original_exception=exception,
-            standard_built_in_tools_params=self.standard_built_in_tools_params,
+        self.model_call_details["standard_logging_object"] = (
+            get_standard_logging_object_payload(
+                kwargs=self.model_call_details,
+                init_response_obj={},
+                start_time=start_time,
+                end_time=end_time,
+                logging_obj=self,
+                status="failure",
+                error_str=str(exception),
+                original_exception=exception,
+                standard_built_in_tools_params=self.standard_built_in_tools_params,
+            )
         )
         return start_time, end_time
 
@@ -2979,14 +2981,17 @@ class Logging(LiteLLMLoggingBaseClass):
         - For Non-streaming responses, we need to transform the response to a ModelResponse object.
         - For streaming responses, anthropic_messages handler calls success_handler with a assembled ModelResponse.
         """
+        import httpx
+
         if self.stream and isinstance(result, ModelResponse):
             return result
         elif isinstance(result, ModelResponse):
             return result
 
-        if "httpx_response" in self.model_call_details:
+        httpx_response = self.model_call_details.get("httpx_response", None)
+        if httpx_response and isinstance(httpx_response, httpx.Response):
             result = litellm.AnthropicConfig().transform_response(
-                raw_response=self.model_call_details.get("httpx_response", None),
+                raw_response=httpx_response,
                 model_response=litellm.ModelResponse(),
                 model=self.model,
                 messages=[],
@@ -3355,9 +3360,9 @@ def _init_custom_logger_compatible_class(  # noqa: PLR0915
                 endpoint=arize_config.endpoint,
             )
 
-            os.environ[
-                "OTEL_EXPORTER_OTLP_TRACES_HEADERS"
-            ] = f"space_id={arize_config.space_key},api_key={arize_config.api_key}"
+            os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
+                f"space_id={arize_config.space_key},api_key={arize_config.api_key}"
+            )
             for callback in _in_memory_loggers:
                 if (
                     isinstance(callback, ArizeLogger)
@@ -3381,9 +3386,9 @@ def _init_custom_logger_compatible_class(  # noqa: PLR0915
 
             # auth can be disabled on local deployments of arize phoenix
             if arize_phoenix_config.otlp_auth_headers is not None:
-                os.environ[
-                    "OTEL_EXPORTER_OTLP_TRACES_HEADERS"
-                ] = arize_phoenix_config.otlp_auth_headers
+                os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
+                    arize_phoenix_config.otlp_auth_headers
+                )
 
             for callback in _in_memory_loggers:
                 if (
@@ -3515,9 +3520,9 @@ def _init_custom_logger_compatible_class(  # noqa: PLR0915
                 exporter="otlp_http",
                 endpoint="https://langtrace.ai/api/trace",
             )
-            os.environ[
-                "OTEL_EXPORTER_OTLP_TRACES_HEADERS"
-            ] = f"api_key={os.getenv('LANGTRACE_API_KEY')}"
+            os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
+                f"api_key={os.getenv('LANGTRACE_API_KEY')}"
+            )
             for callback in _in_memory_loggers:
                 if (
                     isinstance(callback, OpenTelemetry)
@@ -4197,10 +4202,10 @@ class StandardLoggingPayloadSetup:
             for key in StandardLoggingHiddenParams.__annotations__.keys():
                 if key in hidden_params:
                     if key == "additional_headers":
-                        clean_hidden_params[
-                            "additional_headers"
-                        ] = StandardLoggingPayloadSetup.get_additional_headers(
-                            hidden_params[key]
+                        clean_hidden_params["additional_headers"] = (
+                            StandardLoggingPayloadSetup.get_additional_headers(
+                                hidden_params[key]
+                            )
                         )
                     else:
                         clean_hidden_params[key] = hidden_params[key]  # type: ignore
@@ -4252,9 +4257,9 @@ class StandardLoggingPayloadSetup:
                 if (
                     custom_logger
                     and hasattr(custom_logger, "s3_path")
-                    and custom_logger.s3_path
+                    and getattr(custom_logger, "s3_path")
                 ):
-                    s3_path = custom_logger.s3_path
+                    s3_path = getattr(custom_logger, "s3_path")
             except Exception:
                 # If any error occurs in getting the logger instance, use default empty s3_path
                 pass
@@ -4704,9 +4709,9 @@ def scrub_sensitive_keys_in_metadata(litellm_params: Optional[dict]):
     ):
         for k, v in metadata["user_api_key_metadata"].items():
             if k == "logging":  # prevent logging user logging keys
-                cleaned_user_api_key_metadata[
-                    k
-                ] = "scrubbed_by_litellm_for_sensitive_keys"
+                cleaned_user_api_key_metadata[k] = (
+                    "scrubbed_by_litellm_for_sensitive_keys"
+                )
             else:
                 cleaned_user_api_key_metadata[k] = v
 
diff --git a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
index 2def52ed7e..6ed9d5725e 100644
--- a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
+++ b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
@@ -2,11 +2,11 @@ import asyncio
 import json
 import time
 import traceback
-from litellm._uuid import uuid
 from typing import Dict, Iterable, List, Literal, Optional, Tuple, Union
 
 import litellm
 from litellm._logging import verbose_logger
+from litellm._uuid import uuid
 from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     _extract_reasoning_content,
@@ -31,6 +31,7 @@ from litellm.types.utils import Logprobs as TextCompletionLogprobs
 from litellm.types.utils import (
     Message,
     ModelResponse,
+    ModelResponseStream,
     RerankResponse,
     StreamingChoices,
     TextChoices,
@@ -108,12 +109,12 @@ async def convert_to_streaming_response_async(response_object: Optional[dict] =
     if response_object is None:
         raise Exception("Error in response object format")
 
-    model_response_object = ModelResponse(stream=True)
+    model_response_object = ModelResponseStream()
 
     if model_response_object is None:
         raise Exception("Error in response creating model response object")
 
-    choice_list = []
+    choice_list: List[StreamingChoices] = []
 
     for idx, choice in enumerate(response_object["choices"]):
         if (
@@ -182,8 +183,8 @@ def convert_to_streaming_response(response_object: Optional[dict] = None):
     if response_object is None:
         raise Exception("Error in response object format")
 
-    model_response_object = ModelResponse(stream=True)
-    choice_list = []
+    model_response_object = ModelResponseStream()
+    choice_list: List[StreamingChoices] = []
     for idx, choice in enumerate(response_object["choices"]):
         delta = Delta(**choice["message"])
         finish_reason = choice.get("finish_reason", None)
@@ -460,7 +461,7 @@ def convert_to_model_response_object(  # noqa: PLR0915
             if stream is True:
                 # for returning cached responses, we need to yield a generator
                 return convert_to_streaming_response(response_object=response_object)
-            choice_list = []
+            choice_list: List[Choices] = []
 
             assert response_object["choices"] is not None and isinstance(
                 response_object["choices"], Iterable
@@ -564,7 +565,7 @@ def convert_to_model_response_object(  # noqa: PLR0915
                     provider_specific_fields=provider_specific_fields,
                 )
                 choice_list.append(choice)
-            model_response_object.choices = choice_list
+            model_response_object.choices = choice_list  # type: ignore
 
             if "usage" in response_object and response_object["usage"] is not None:
                 usage_object = litellm.Usage(**response_object["usage"])
diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py
index b2f6c62140..1daf543cfc 100644
--- a/litellm/litellm_core_utils/streaming_handler.py
+++ b/litellm/litellm_core_utils/streaming_handler.py
@@ -5,7 +5,6 @@ import json
 import threading
 import time
 import traceback
-from litellm._uuid import uuid
 from typing import Any, Callable, Dict, List, Optional, Union, cast
 
 import httpx
@@ -13,6 +12,7 @@ from pydantic import BaseModel
 
 import litellm
 from litellm import verbose_logger
+from litellm._uuid import uuid
 from litellm.litellm_core_utils.model_response_utils import (
     is_model_response_stream_empty,
 )
@@ -1024,7 +1024,7 @@ class CustomStreamWrapper:
         return
 
     def chunk_creator(self, chunk: Any):  # type: ignore  # noqa: PLR0915
-        if hasattr(chunk, 'id'):
+        if hasattr(chunk, "id"):
             self.response_id = chunk.id
         model_response = self.model_response_creator()
         response_obj: Dict[str, Any] = {}
@@ -1365,12 +1365,13 @@ class CustomStreamWrapper:
                 f"model_response finish reason 3: {self.received_finish_reason}; response_obj={response_obj}"
             )
             ## FUNCTION CALL PARSING
+            original_chunk = (
+                response_obj.get("original_chunk") if response_obj is not None else None
+            )
             if (
-                response_obj is not None
-                and response_obj.get("original_chunk", None) is not None
+                original_chunk is not None
             ):  # function / tool calling branch - only set for openai/azure compatible endpoints
                 # enter this branch when no content has been passed in response
-                original_chunk = response_obj.get("original_chunk", None)
                 if hasattr(original_chunk, "id"):
                     model_response = self.set_model_id(
                         original_chunk.id, model_response
diff --git a/litellm/llms/anthropic/completion/transformation.py b/litellm/llms/anthropic/completion/transformation.py
index 9e3287aa8a..a8798cd5d0 100644
--- a/litellm/llms/anthropic/completion/transformation.py
+++ b/litellm/llms/anthropic/completion/transformation.py
@@ -55,9 +55,9 @@ class AnthropicTextConfig(BaseConfig):
     to pass metadata to anthropic, it's {"user_id": "any-relevant-information"}
     """
 
-    max_tokens_to_sample: Optional[
-        int
-    ] = litellm.max_tokens  # anthropic requires a default
+    max_tokens_to_sample: Optional[int] = (
+        litellm.max_tokens
+    )  # anthropic requires a default
     stop_sequences: Optional[list] = None
     temperature: Optional[int] = None
     top_p: Optional[int] = None
@@ -291,7 +291,7 @@ class AnthropicTextCompletionResponseIterator(BaseModelResponseIterator):
             _chunk_text = chunk.get("completion", None)
             if _chunk_text is not None and isinstance(_chunk_text, str):
                 text = _chunk_text
-            finish_reason = chunk.get("stop_reason", None)
+            finish_reason = chunk.get("stop_reason") or ""
             if finish_reason is not None:
                 is_finished = True
             returned_chunk = GenericStreamingChunk(
diff --git a/litellm/llms/anthropic/cost_calculation.py b/litellm/llms/anthropic/cost_calculation.py
index 56a83324d9..8f34eb00ce 100644
--- a/litellm/llms/anthropic/cost_calculation.py
+++ b/litellm/llms/anthropic/cost_calculation.py
@@ -49,7 +49,7 @@ def get_cost_for_anthropic_web_search(
 
     ## Get the cost per web search request
     search_context_pricing: SearchContextCostPerQuery = (
-        model_info.get("search_context_cost_per_query", {}) or {}
+        model_info.get("search_context_cost_per_query") or SearchContextCostPerQuery()
     )
     cost_per_web_search_request = search_context_pricing.get(
         "search_context_size_medium", 0.0
diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py
index 5ee9065f5e..3645c16bf8 100644
--- a/litellm/llms/azure/azure.py
+++ b/litellm/llms/azure/azure.py
@@ -182,12 +182,12 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM):
         model: str,
         messages: list,
         model_response: ModelResponse,
-        api_key: str,
+        api_key: Optional[str],
         api_base: str,
         api_version: str,
         api_type: str,
-        azure_ad_token: str,
-        azure_ad_token_provider: Callable,
+        azure_ad_token: Optional[str],
+        azure_ad_token_provider: Optional[Callable],
         dynamic_params: bool,
         print_verbose: Callable,
         timeout: Union[float, httpx.Timeout],
@@ -372,7 +372,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM):
 
     async def acompletion(
         self,
-        api_key: str,
+        api_key: Optional[str],
         api_version: str,
         model: str,
         api_base: str,
@@ -477,7 +477,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM):
         self,
         logging_obj,
         api_base: str,
-        api_key: str,
+        api_key: Optional[str],
         api_version: str,
         dynamic_params: bool,
         data: dict,
@@ -555,7 +555,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM):
         self,
         logging_obj: LiteLLMLoggingObj,
         api_base: str,
-        api_key: str,
+        api_key: Optional[str],
         api_version: str,
         dynamic_params: bool,
         data: dict,
diff --git a/litellm/llms/azure/common_utils.py b/litellm/llms/azure/common_utils.py
index 09b1888e04..04448681b6 100644
--- a/litellm/llms/azure/common_utils.py
+++ b/litellm/llms/azure/common_utils.py
@@ -162,8 +162,8 @@ def get_azure_ad_token_from_username_password(
 
 def get_azure_ad_token_from_oidc(
     azure_ad_token: str,
-    azure_client_id: Optional[str],
-    azure_tenant_id: Optional[str],
+    azure_client_id: Optional[str] = None,
+    azure_tenant_id: Optional[str] = None,
     scope: Optional[str] = None,
 ) -> str:
     """
diff --git a/litellm/llms/azure/completion/handler.py b/litellm/llms/azure/completion/handler.py
index a44f904571..05d5e2f6c6 100644
--- a/litellm/llms/azure/completion/handler.py
+++ b/litellm/llms/azure/completion/handler.py
@@ -30,11 +30,11 @@ class AzureTextCompletion(BaseAzureLLM):
         model: str,
         messages: list,
         model_response: ModelResponse,
-        api_key: str,
+        api_key: Optional[str],
         api_base: str,
         api_version: str,
         api_type: str,
-        azure_ad_token: str,
+        azure_ad_token: Optional[str],
         azure_ad_token_provider: Optional[Callable],
         print_verbose: Callable,
         timeout,
@@ -59,7 +59,7 @@ class AzureTextCompletion(BaseAzureLLM):
 
             ### CHECK IF CLOUDFLARE AI GATEWAY ###
             ### if so - set the model as part of the base url
-            if "gateway.ai.cloudflare.com" in api_base:
+            if api_base is not None and "gateway.ai.cloudflare.com" in api_base:
                 ## build base url - assume api base includes resource name
                 client = self._init_azure_client_for_cloudflare_ai_gateway(
                     api_key=api_key,
@@ -196,7 +196,7 @@ class AzureTextCompletion(BaseAzureLLM):
 
     async def acompletion(
         self,
-        api_key: str,
+        api_key: Optional[str],
         api_version: str,
         model: str,
         api_base: str,
@@ -263,7 +263,7 @@ class AzureTextCompletion(BaseAzureLLM):
         self,
         logging_obj,
         api_base: str,
-        api_key: str,
+        api_key: Optional[str],
         api_version: str,
         data: dict,
         model: str,
@@ -320,7 +320,7 @@ class AzureTextCompletion(BaseAzureLLM):
         self,
         logging_obj,
         api_base: str,
-        api_key: str,
+        api_key: Optional[str],
         api_version: str,
         data: dict,
         model: str,
diff --git a/litellm/llms/bedrock/chat/invoke_agent/transformation.py b/litellm/llms/bedrock/chat/invoke_agent/transformation.py
index 4a7c33ed20..2c7135f4d8 100644
--- a/litellm/llms/bedrock/chat/invoke_agent/transformation.py
+++ b/litellm/llms/bedrock/chat/invoke_agent/transformation.py
@@ -3,14 +3,15 @@ Transformation for Bedrock Invoke Agent
 
 https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent-runtime_InvokeAgent.html
 """
+
 import base64
 import json
-from litellm._uuid import uuid
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
 import httpx
 
 from litellm._logging import verbose_logger
+from litellm._uuid import uuid
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     convert_content_list_to_str,
 )
@@ -22,6 +23,11 @@ from litellm.types.llms.bedrock_invoke_agents import (
     InvokeAgentEvent,
     InvokeAgentEventHeaders,
     InvokeAgentEventList,
+    InvokeAgentMetadata,
+    InvokeAgentModelInvocationInput,
+    InvokeAgentModelInvocationOutput,
+    InvokeAgentOrchestrationTrace,
+    InvokeAgentPreProcessingTrace,
     InvokeAgentTrace,
     InvokeAgentTracePayload,
     InvokeAgentUsage,
@@ -389,15 +395,22 @@ class AmazonInvokeAgentConfig(BaseConfig, BaseAWSLLM):
         self, trace_data: InvokeAgentTrace, usage_info: InvokeAgentUsage
     ) -> None:
         """Extract usage information from preprocessing trace."""
-        pre_processing = trace_data.get("preProcessingTrace", {})
+        pre_processing: Optional[InvokeAgentPreProcessingTrace] = trace_data.get(
+            "preProcessingTrace"
+        )
         if not pre_processing:
             return
 
-        model_output = pre_processing.get("modelInvocationOutput", {})
+        model_output: Optional[InvokeAgentModelInvocationOutput] = (
+            pre_processing.get("modelInvocationOutput")
+            or InvokeAgentModelInvocationOutput()
+        )
         if not model_output:
             return
 
-        metadata = model_output.get("metadata", {})
+        metadata: Optional[InvokeAgentMetadata] = (
+            model_output.get("metadata") or InvokeAgentMetadata()
+        )
         if not metadata:
             return
 
@@ -412,11 +425,16 @@ class AmazonInvokeAgentConfig(BaseConfig, BaseAWSLLM):
         self, trace_data: InvokeAgentTrace
     ) -> Optional[str]:
         """Extract model information from orchestration trace."""
-        orchestration_trace = trace_data.get("orchestrationTrace", {})
+        orchestration_trace: Optional[InvokeAgentOrchestrationTrace] = trace_data.get(
+            "orchestrationTrace"
+        )
         if not orchestration_trace:
             return None
 
-        model_invocation = orchestration_trace.get("modelInvocationInput", {})
+        model_invocation: Optional[InvokeAgentModelInvocationInput] = (
+            orchestration_trace.get("modelInvocationInput")
+            or InvokeAgentModelInvocationInput()
+        )
         if not model_invocation:
             return None
 
diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py
index 0d1b4102c9..71aadffe5b 100644
--- a/litellm/llms/bedrock/chat/invoke_handler.py
+++ b/litellm/llms/bedrock/chat/invoke_handler.py
@@ -7,7 +7,6 @@ import json
 import time
 import types
 import urllib.parse
-from litellm._uuid import uuid
 from functools import partial
 from typing import (
     Any,
@@ -26,6 +25,7 @@ import httpx  # type: ignore
 
 import litellm
 from litellm import verbose_logger
+from litellm._uuid import uuid
 from litellm.caching.caching import InMemoryCache
 from litellm.litellm_core_utils.core_helpers import map_finish_reason
 from litellm.litellm_core_utils.litellm_logging import Logging
@@ -498,9 +498,9 @@ class BedrockLLM(BaseAWSLLM):
                             content=None,
                         )
                         model_response.choices[0].message = _message  # type: ignore
-                        model_response._hidden_params[
-                            "original_response"
-                        ] = outputText  # allow user to access raw anthropic tool calling response
+                        model_response._hidden_params["original_response"] = (
+                            outputText  # allow user to access raw anthropic tool calling response
+                        )
                     if (
                         _is_function_call is True
                         and stream is not None
@@ -808,9 +808,9 @@ class BedrockLLM(BaseAWSLLM):
                     ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
                         inference_params[k] = v
                 if stream is True:
-                    inference_params[
-                        "stream"
-                    ] = True  # cohere requires stream = True in inference params
+                    inference_params["stream"] = (
+                        True  # cohere requires stream = True in inference params
+                    )
                 data = json.dumps({"prompt": prompt, **inference_params})
         elif provider == "anthropic":
             if model.startswith("anthropic.claude-3"):
@@ -1352,9 +1352,11 @@ class AWSEventStreamDecoder:
                             "name": None,
                             "arguments": delta_obj["toolUse"]["input"],
                         },
-                        "index": self.tool_calls_index
-                        if self.tool_calls_index is not None
-                        else index,
+                        "index": (
+                            self.tool_calls_index
+                            if self.tool_calls_index is not None
+                            else index
+                        ),
                     }
                 elif "reasoningContent" in delta_obj:
                     provider_specific_fields = {
@@ -1384,9 +1386,11 @@ class AWSEventStreamDecoder:
                             "name": None,
                             "arguments": "{}",
                         },
-                        "index": self.tool_calls_index
-                        if self.tool_calls_index is not None
-                        else index,
+                        "index": (
+                            self.tool_calls_index
+                            if self.tool_calls_index is not None
+                            else index
+                        ),
                     }
             elif "stopReason" in chunk_data:
                 finish_reason = map_finish_reason(chunk_data.get("stopReason", "stop"))
@@ -1448,7 +1452,7 @@ class AWSEventStreamDecoder:
         ######### /bedrock/invoke nova mappings ###############
         elif "contentBlockDelta" in chunk_data:
             # when using /bedrock/invoke/nova, the chunk_data is nested under "contentBlockDelta"
-            _chunk_data = chunk_data.get("contentBlockDelta", None)
+            _chunk_data = chunk_data.get("contentBlockDelta", {})
             return self.converse_chunk_parser(chunk_data=_chunk_data)
         ######## bedrock.mistral mappings ###############
         elif "outputs" in chunk_data:
diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py
index f8a92c2ac9..173bb5a2cc 100644
--- a/litellm/llms/custom_httpx/llm_http_handler.py
+++ b/litellm/llms/custom_httpx/llm_http_handler.py
@@ -89,6 +89,7 @@ from litellm.utils import (
 
 if TYPE_CHECKING:
     from aiohttp import ClientSession
+
     from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
     from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig
 
@@ -281,7 +282,7 @@ class BaseLLMHTTPHandler:
         self,
         model: str,
         messages: list,
-        api_base: str,
+        api_base: Optional[str],
         custom_llm_provider: str,
         model_response: ModelResponse,
         encoding,
@@ -750,7 +751,7 @@ class BaseLLMHTTPHandler:
         model_response: EmbeddingResponse,
         api_key: Optional[str] = None,
         client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
-        aembedding: bool = False,
+        aembedding: Optional[bool] = False,
         headers: Optional[Dict[str, Any]] = None,
     ) -> EmbeddingResponse:
         provider_config = ProviderConfigManager.get_provider_embedding_config(
@@ -3100,7 +3101,10 @@ class BaseLLMHTTPHandler:
         _is_async: bool = False,
         fake_stream: bool = False,
         litellm_metadata: Optional[Dict[str, Any]] = None,
-    ) -> Union[ImageResponse, Coroutine[Any, Any, ImageResponse],]:
+    ) -> Union[
+        ImageResponse,
+        Coroutine[Any, Any, ImageResponse],
+    ]:
         """
 
         Handles image edit requests.
@@ -3290,7 +3294,10 @@ class BaseLLMHTTPHandler:
         fake_stream: bool = False,
         litellm_metadata: Optional[Dict[str, Any]] = None,
         api_key: Optional[str] = None,
-    ) -> Union[ImageResponse, Coroutine[Any, Any, ImageResponse],]:
+    ) -> Union[
+        ImageResponse,
+        Coroutine[Any, Any, ImageResponse],
+    ]:
         """
         Handles image generation requests.
         When _is_async=True, returns a coroutine instead of making the call directly.
diff --git a/litellm/llms/gemini/google_genai/transformation.py b/litellm/llms/gemini/google_genai/transformation.py
index f38c772e35..94dfea5f58 100644
--- a/litellm/llms/gemini/google_genai/transformation.py
+++ b/litellm/llms/gemini/google_genai/transformation.py
@@ -1,6 +1,7 @@
 """
 Transformation for Calling Google models in their native format.
 """
+
 from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union, cast
 
 import httpx
@@ -25,27 +26,29 @@ else:
     GenerateContentContentListUnionDict = Any
     GenerateContentResponse = Any
     ToolConfigDict = Any
-    
+
 from ..common_utils import get_api_key_from_env
 
+
 class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
     """
     Configuration for calling Google models in their native format.
     """
+
     ##############################
     # Constants
     ##############################
     XGOOGLE_API_KEY = "x-goog-api-key"
     ##############################
-    
+
     @property
     def custom_llm_provider(self) -> Literal["gemini", "vertex_ai"]:
         return "gemini"
-    
+
     def __init__(self):
         super().__init__()
         VertexLLM.__init__(self)
-    
+
     def get_supported_generate_content_optional_params(self, model: str) -> List[str]:
         """
         Get the list of supported Google GenAI parameters for the model.
@@ -58,7 +61,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         """
         return [
             "http_options",
-            "system_instruction", 
+            "system_instruction",
             "temperature",
             "top_p",
             "top_k",
@@ -84,10 +87,9 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
             "speech_config",
             "audio_timestamp",
             "automatic_function_calling",
-            "thinking_config"
+            "thinking_config",
         ]
 
-
     def map_generate_content_optional_params(
         self,
         generate_content_config_dict: GenerateContentConfigDict,
@@ -103,26 +105,29 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         Returns:
             Mapped parameters for the provider
         """
-        from litellm.types.google_genai.main import GenerateContentConfigDict
-        _generate_content_config_dict = GenerateContentConfigDict()
-        supported_google_genai_params = self.get_supported_generate_content_optional_params(model)
+        _generate_content_config_dict: Dict[str, Any] = {}
+        supported_google_genai_params = (
+            self.get_supported_generate_content_optional_params(model)
+        )
         for param, value in generate_content_config_dict.items():
             if param in supported_google_genai_params:
                 _generate_content_config_dict[param] = value
-        return dict(_generate_content_config_dict)
-    
+        return _generate_content_config_dict
+
     def validate_environment(
-        self, 
+        self,
         api_key: Optional[str],
         headers: Optional[dict],
         model: str,
-        litellm_params: Optional[Union[GenericLiteLLMParams, dict]]
+        litellm_params: Optional[Union[GenericLiteLLMParams, dict]],
     ) -> dict:
         default_headers = {
             "Content-Type": "application/json",
         }
         # Use the passed api_key first, then fall back to litellm_params and environment
-        gemini_api_key = api_key or self._get_google_ai_studio_api_key(dict(litellm_params or {}))
+        gemini_api_key = api_key or self._get_google_ai_studio_api_key(
+            dict(litellm_params or {})
+        )
         if gemini_api_key is not None:
             default_headers[self.XGOOGLE_API_KEY] = gemini_api_key
         if headers is not None:
@@ -137,14 +142,14 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
             or get_api_key_from_env()
             or litellm.api_key
         )
-    
+
     def _get_common_auth_components(
         self,
         litellm_params: dict,
     ) -> Tuple[Any, Optional[str], Optional[str]]:
         """
         Get common authentication components used by both sync and async methods.
-        
+
         Returns:
             Tuple of (vertex_credentials, vertex_project, vertex_location)
         """
@@ -152,7 +157,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         vertex_project = self.get_vertex_ai_project(litellm_params)
         vertex_location = self.get_vertex_ai_location(litellm_params)
         return vertex_credentials, vertex_project, vertex_location
-    
+
     def _build_final_headers_and_url(
         self,
         model: str,
@@ -168,7 +173,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         Build final headers and API URL from auth components.
         """
         gemini_api_key = self._get_google_ai_studio_api_key(litellm_params)
-        
+
         auth_header, api_base = self._get_token_and_url(
             model=model,
             gemini_api_key=gemini_api_key,
@@ -201,7 +206,9 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         """
         Sync version of get_auth_token_and_url.
         """
-        vertex_credentials, vertex_project, vertex_location = self._get_common_auth_components(litellm_params)
+        vertex_credentials, vertex_project, vertex_location = (
+            self._get_common_auth_components(litellm_params)
+        )
 
         _auth_header, vertex_project = self._ensure_access_token(
             credentials=vertex_credentials,
@@ -238,7 +245,9 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         Returns:
             Tuple of headers and API base
         """
-        vertex_credentials, vertex_project, vertex_location = self._get_common_auth_components(litellm_params)
+        vertex_credentials, vertex_project, vertex_location = (
+            self._get_common_auth_components(litellm_params)
+        )
 
         _auth_header, vertex_project = await self._ensure_access_token_async(
             credentials=vertex_credentials,
@@ -256,7 +265,6 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
             api_base=api_base,
             litellm_params=litellm_params,
         )
-    
 
     def transform_generate_content_request(
         self,
@@ -269,6 +277,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
             GenerateContentConfigDict,
             GenerateContentRequestDict,
         )
+
         typed_generate_content_request = GenerateContentRequestDict(
             model=model,
             contents=contents,
@@ -279,7 +288,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         request_dict = cast(dict, typed_generate_content_request)
 
         return request_dict
-    
+
     def transform_generate_content_response(
         self,
         model: str,
@@ -297,6 +306,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
             Transformed response data
         """
         from litellm.types.google_genai.main import GenerateContentResponse
+
         try:
             response = raw_response.json()
         except Exception as e:
@@ -305,7 +315,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
                 status_code=raw_response.status_code,
                 headers=raw_response.headers,
             )
-        
+
         logging_obj.model_call_details["httpx_response"] = raw_response
-        
-        return GenerateContentResponse(**response)
\ No newline at end of file
+
+        return GenerateContentResponse(**response)
diff --git a/litellm/llms/huggingface/embedding/transformation.py b/litellm/llms/huggingface/embedding/transformation.py
index 60bd5dcd61..88d42cfcdc 100644
--- a/litellm/llms/huggingface/embedding/transformation.py
+++ b/litellm/llms/huggingface/embedding/transformation.py
@@ -40,17 +40,17 @@ class HuggingFaceEmbeddingConfig(BaseConfig):
     Reference: https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/compat_generate
     """
 
-    hf_task: Optional[
-        hf_tasks
-    ] = None  # litellm-specific param, used to know the api spec to use when calling huggingface api
+    hf_task: Optional[hf_tasks] = (
+        None  # litellm-specific param, used to know the api spec to use when calling huggingface api
+    )
     best_of: Optional[int] = None
     decoder_input_details: Optional[bool] = None
     details: Optional[bool] = True  # enables returning logprobs + best of
     max_new_tokens: Optional[int] = None
     repetition_penalty: Optional[float] = None
-    return_full_text: Optional[
-        bool
-    ] = False  # by default don't return the input as part of the output
+    return_full_text: Optional[bool] = (
+        False  # by default don't return the input as part of the output
+    )
     seed: Optional[int] = None
     temperature: Optional[float] = None
     top_k: Optional[int] = None
@@ -120,9 +120,9 @@ class HuggingFaceEmbeddingConfig(BaseConfig):
                 optional_params["top_p"] = value
             if param == "n":
                 optional_params["best_of"] = value
-                optional_params[
-                    "do_sample"
-                ] = True  # Need to sample if you want best of for hf inference endpoints
+                optional_params["do_sample"] = (
+                    True  # Need to sample if you want best of for hf inference endpoints
+                )
             if param == "stream":
                 optional_params["stream"] = value
             if param == "stop":
@@ -268,7 +268,7 @@ class HuggingFaceEmbeddingConfig(BaseConfig):
                 # check if the model has a registered custom prompt
                 model_prompt_details = litellm.custom_prompt_dict[model]
                 prompt = custom_prompt(
-                    role_dict=model_prompt_details.get("roles", None),
+                    role_dict=model_prompt_details.get("roles") or {},
                     initial_prompt_value=model_prompt_details.get(
                         "initial_prompt_value", ""
                     ),
@@ -363,9 +363,9 @@ class HuggingFaceEmbeddingConfig(BaseConfig):
             "content-type": "application/json",
         }
         if api_key is not None:
-            default_headers[
-                "Authorization"
-            ] = f"Bearer {api_key}"  # Huggingface Inference Endpoint default is to accept bearer tokens
+            default_headers["Authorization"] = (
+                f"Bearer {api_key}"  # Huggingface Inference Endpoint default is to accept bearer tokens
+            )
 
         headers = {**headers, **default_headers}
         return headers
diff --git a/litellm/llms/openai/completion/transformation.py b/litellm/llms/openai/completion/transformation.py
index 43fbc1f219..77dc0b54fe 100644
--- a/litellm/llms/openai/completion/transformation.py
+++ b/litellm/llms/openai/completion/transformation.py
@@ -1,5 +1,5 @@
 """
-Support for gpt model family 
+Support for gpt model family
 """
 
 from typing import List, Optional, Union
@@ -87,7 +87,7 @@ class OpenAITextCompletionConfig(BaseTextCompletionConfig, OpenAIGPTConfig):
             ## RESPONSE OBJECT
             if response_object is None or model_response_object is None:
                 raise ValueError("Error in response object format")
-            choice_list = []
+            choice_list: List[Choices] = []
             for idx, choice in enumerate(response_object["choices"]):
                 message = Message(
                     content=choice["text"],
@@ -100,7 +100,7 @@ class OpenAITextCompletionConfig(BaseTextCompletionConfig, OpenAIGPTConfig):
                     logprobs=choice.get("logprobs", None),
                 )
                 choice_list.append(choice)
-            model_response_object.choices = choice_list
+            model_response_object.choices = choice_list  # type: ignore
 
             if "usage" in response_object:
                 setattr(model_response_object, "usage", response_object["usage"])
@@ -111,9 +111,9 @@ class OpenAITextCompletionConfig(BaseTextCompletionConfig, OpenAIGPTConfig):
             if "model" in response_object:
                 model_response_object.model = response_object["model"]
 
-            model_response_object._hidden_params[
-                "original_response"
-            ] = response_object  # track original response, if users make a litellm.text_completion() request, we can return the original response
+            model_response_object._hidden_params["original_response"] = (
+                response_object  # track original response, if users make a litellm.text_completion() request, we can return the original response
+            )
             return model_response_object
         except Exception as e:
             raise e
diff --git a/litellm/llms/openai/image_edit/transformation.py b/litellm/llms/openai/image_edit/transformation.py
index f6898c3a76..be96064115 100644
--- a/litellm/llms/openai/image_edit/transformation.py
+++ b/litellm/llms/openai/image_edit/transformation.py
@@ -91,21 +91,22 @@ class OpenAIImageEditConfig(BaseImageEditConfig):
 
         # Handle image parameter
         if _image_list is not None:
-            image_list = [_image_list] if not isinstance(_image_list, list) else _image_list
+            image_list = (
+                [_image_list] if not isinstance(_image_list, list) else _image_list
+            )
             for _image in image_list:
                 if _image is not None:
-                    image_content_type: str = ImageEditRequestUtils.get_image_content_type(
-                        _image
+                    image_content_type: str = (
+                        ImageEditRequestUtils.get_image_content_type(_image)
                     )
                     if isinstance(_image, BufferedReader):
                         files_list.append(
-                            ("image", (_image.name, _image, image_content_type))
+                            ("image[]", (_image.name, _image, image_content_type))
                         )
                     else:
                         files_list.append(
-                            ("image", ("image.png", _image, image_content_type))
+                            ("image[]", ("image.png", _image, image_content_type))
                         )
-
         # Handle mask parameter if provided
         if _mask is not None:
             # Handle case where mask can be a list (extract first mask)
@@ -120,6 +121,7 @@ class OpenAIImageEditConfig(BaseImageEditConfig):
                     files_list.append(("mask", (_mask.name, _mask, mask_content_type)))
                 else:
                     files_list.append(("mask", ("mask.png", _mask, mask_content_type)))
+
         return data_without_files, files_list
 
     def transform_image_edit_response(
diff --git a/litellm/llms/vertex_ai/fine_tuning/handler.py b/litellm/llms/vertex_ai/fine_tuning/handler.py
index 4d7f8cec02..6372f8ea30 100644
--- a/litellm/llms/vertex_ai/fine_tuning/handler.py
+++ b/litellm/llms/vertex_ai/fine_tuning/handler.py
@@ -64,9 +64,9 @@ class VertexFineTuningAPI(VertexLLM):
         )
 
         if create_fine_tuning_job_data.validation_file:
-            supervised_tuning_spec[
-                "validation_dataset"
-            ] = create_fine_tuning_job_data.validation_file
+            supervised_tuning_spec["validation_dataset"] = (
+                create_fine_tuning_job_data.validation_file
+            )
 
         _vertex_hyperparameters = (
             self._transform_openai_hyperparameters_to_vertex_hyperparameters(
@@ -140,7 +140,9 @@ class VertexFineTuningAPI(VertexLLM):
             fine_tuned_model=response.get("tunedModelDisplayName", ""),
             finished_at=None,
             hyperparameters=self._translate_vertex_response_hyperparameters(
-                vertex_hyper_parameters=_supervisedTuningSpec.get("hyperParameters", {})
+                vertex_hyper_parameters=_supervisedTuningSpec.get(
+                    "hyperParameters", FineTuneHyperparameters()
+                )
                 or {}
             ),
             model=response.get("baseModel", "") or "",
@@ -343,9 +345,9 @@ class VertexFineTuningAPI(VertexLLM):
         elif "cachedContents" in request_route:
             _model = request_data.get("model")
             if _model is not None and "/publishers/google/models/" not in _model:
-                request_data[
-                    "model"
-                ] = f"projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{_model}"
+                request_data["model"] = (
+                    f"projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{_model}"
+                )
 
             url = f"https://{vertex_location}-aiplatform.googleapis.com/v1beta1/projects/{vertex_project}/locations/{vertex_location}{request_route}"
         else:
diff --git a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py
index ecfe2ee8b4..af9af71fef 100644
--- a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py
+++ b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py
@@ -43,7 +43,7 @@ class GoogleBatchEmbeddings(VertexLLM):
         vertex_project=None,
         vertex_location=None,
         vertex_credentials=None,
-        aembedding=False,
+        aembedding: Optional[bool] = False,
         timeout=300,
         client=None,
     ) -> EmbeddingResponse:
diff --git a/litellm/llms/vertex_ai/google_genai/transformation.py b/litellm/llms/vertex_ai/google_genai/transformation.py
index 574000e6bc..d7a4ceeb3e 100644
--- a/litellm/llms/vertex_ai/google_genai/transformation.py
+++ b/litellm/llms/vertex_ai/google_genai/transformation.py
@@ -1,7 +1,8 @@
 """
 Transformation for Calling Google models in their native format.
 """
-from typing import Dict, Literal, Optional, Union
+
+from typing import Any, Dict, Literal, Optional, Union
 
 from litellm.llms.gemini.google_genai.transformation import GoogleGenAIConfig
 from litellm.types.router import GenericLiteLLMParams
@@ -58,22 +59,21 @@ class VertexAIGoogleGenAIConfig(GoogleGenAIConfig):
         Returns:
             Mapped parameters for the provider
         """
-        from litellm.types.google_genai.main import GenerateContentConfigDict
 
-        _generate_content_config_dict = GenerateContentConfigDict()
+        _generate_content_config_dict: Dict = {}
 
         for param, value in generate_content_config_dict.items():
             camel_case_key = self._camel_to_snake(param)
             _generate_content_config_dict[camel_case_key] = value
-        return dict(_generate_content_config_dict)
+        return _generate_content_config_dict
 
     def transform_generate_content_request(
         self,
         model: str,
-        contents: any,
-        tools: Optional[any],
+        contents: Any,
+        tools: Optional[Any],
         generate_content_config_dict: Dict,
-        system_instruction: Optional[any] = None,
+        system_instruction: Optional[Any] = None,
     ) -> dict:
         """
         Transform the generate content request for Vertex AI.
diff --git a/litellm/llms/vertex_ai/multimodal_embeddings/embedding_handler.py b/litellm/llms/vertex_ai/multimodal_embeddings/embedding_handler.py
index 8aebd83cc4..582d7a4c56 100644
--- a/litellm/llms/vertex_ai/multimodal_embeddings/embedding_handler.py
+++ b/litellm/llms/vertex_ai/multimodal_embeddings/embedding_handler.py
@@ -46,7 +46,7 @@ class VertexMultimodalEmbedding(VertexLLM):
         vertex_project=None,
         vertex_location=None,
         vertex_credentials=None,
-        aembedding=False,
+        aembedding: Optional[bool] = False,
         timeout=300,
         client=None,
     ) -> EmbeddingResponse:
diff --git a/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py b/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py
index 1167ca285f..a170e6cc7f 100644
--- a/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py
+++ b/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py
@@ -36,7 +36,7 @@ class VertexEmbedding(VertexBase):
         timeout: Optional[Union[float, httpx.Timeout]],
         api_key: Optional[str] = None,
         encoding=None,
-        aembedding=False,
+        aembedding: Optional[bool] = False,
         api_base: Optional[str] = None,
         client: Optional[Union[AsyncHTTPHandler, HTTPHandler]] = None,
         vertex_project: Optional[str] = None,
@@ -86,8 +86,10 @@ class VertexEmbedding(VertexBase):
             mode="embedding",
         )
         headers = self.set_headers(auth_header=auth_header, extra_headers=extra_headers)
-        vertex_request: VertexEmbeddingRequest = litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request(
-            input=input, optional_params=optional_params, model=model
+        vertex_request: VertexEmbeddingRequest = (
+            litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request(
+                input=input, optional_params=optional_params, model=model
+            )
         )
 
         _client_params = {}
@@ -176,8 +178,10 @@ class VertexEmbedding(VertexBase):
             mode="embedding",
         )
         headers = self.set_headers(auth_header=auth_header, extra_headers=extra_headers)
-        vertex_request: VertexEmbeddingRequest = litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request(
-            input=input, optional_params=optional_params, model=model
+        vertex_request: VertexEmbeddingRequest = (
+            litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request(
+                input=input, optional_params=optional_params, model=model
+            )
         )
 
         _async_client_params = {}
diff --git a/litellm/llms/watsonx/chat/handler.py b/litellm/llms/watsonx/chat/handler.py
index 5c19757fec..bc0effe4a1 100644
--- a/litellm/llms/watsonx/chat/handler.py
+++ b/litellm/llms/watsonx/chat/handler.py
@@ -21,7 +21,7 @@ class WatsonXChatHandler(OpenAILikeChatHandler):
         *,
         model: str,
         messages: list,
-        api_base: str,
+        api_base: Optional[str],
         custom_llm_provider: str,
         custom_prompt_dict: dict,
         model_response: ModelResponse,
@@ -70,7 +70,7 @@ class WatsonXChatHandler(OpenAILikeChatHandler):
         )
 
         return super().completion(
-            model=watsonx_auth_payload.get("model_id", None),
+            model=watsonx_auth_payload.get("model_id") or "",
             messages=messages,
             api_base=api_base,
             custom_llm_provider=custom_llm_provider,
diff --git a/litellm/main.py b/litellm/main.py
index f0b1820cbb..47f5cf1155 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -17,12 +17,12 @@ import random
 import sys
 import time
 import traceback
-from litellm._uuid import uuid
 from concurrent import futures
 from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait
 from copy import deepcopy
 from functools import partial
 from typing import (
+    TYPE_CHECKING,
     Any,
     Callable,
     Coroutine,
@@ -36,9 +36,10 @@ from typing import (
     Union,
     cast,
     get_args,
-    TYPE_CHECKING,
 )
 
+from litellm._uuid import uuid
+
 if TYPE_CHECKING:
     from aiohttp import ClientSession
 
@@ -721,12 +722,15 @@ async def _sleep_for_timeout_async(timeout: Union[float, str, httpx.Timeout]):
         await asyncio.sleep(timeout.connect)
 
 
+MOCK_RESPONSE_TYPE = Union[str, Exception, dict]
+
+
 def mock_completion(
     model: str,
     messages: List,
     stream: Optional[bool] = False,
     n: Optional[int] = None,
-    mock_response: Union[str, Exception, dict] = "This is a mock request",
+    mock_response: Optional[MOCK_RESPONSE_TYPE] = "This is a mock request",
     mock_tool_calls: Optional[List] = None,
     mock_timeout: Optional[bool] = False,
     logging=None,
@@ -1007,7 +1011,7 @@ def completion(  # type: ignore # noqa: PLR0915
     ######### unpacking kwargs #####################
     args = locals()
     api_base = kwargs.get("api_base", None)
-    mock_response = kwargs.get("mock_response", None)
+    mock_response: Optional[MOCK_RESPONSE_TYPE] = kwargs.get("mock_response", None)
     mock_tool_calls = kwargs.get("mock_tool_calls", None)
     mock_timeout = cast(Optional[bool], kwargs.get("mock_timeout", None))
     force_timeout = kwargs.get("force_timeout", 600)  ## deprecated
@@ -1114,7 +1118,7 @@ def completion(  # type: ignore # noqa: PLR0915
             api_base = base_url
         if num_retries is not None:
             max_retries = num_retries
-        logging = litellm_logging_obj
+        logging: Logging = cast(Logging, litellm_logging_obj)
         fallbacks = fallbacks or litellm.model_fallbacks
         if fallbacks is not None:
             return completion_with_fallbacks(**args)
@@ -1427,7 +1431,7 @@ def completion(  # type: ignore # noqa: PLR0915
             api_version = (
                 api_version
                 or litellm.api_version
-                or get_secret("AZURE_API_VERSION")
+                or get_secret_str("AZURE_API_VERSION")
                 or litellm.AZURE_DEFAULT_API_VERSION
             )
 
@@ -1435,13 +1439,13 @@ def completion(  # type: ignore # noqa: PLR0915
                 api_key
                 or litellm.api_key
                 or litellm.azure_key
-                or get_secret("AZURE_OPENAI_API_KEY")
-                or get_secret("AZURE_API_KEY")
+                or get_secret_str("AZURE_OPENAI_API_KEY")
+                or get_secret_str("AZURE_API_KEY")
             )
 
             azure_ad_token = optional_params.get("extra_body", {}).pop(
                 "azure_ad_token", None
-            ) or get_secret("AZURE_AD_TOKEN")
+            ) or get_secret_str("AZURE_AD_TOKEN")
 
             azure_ad_token_provider = litellm_params.get(
                 "azure_ad_token_provider", None
@@ -1529,25 +1533,32 @@ def completion(  # type: ignore # noqa: PLR0915
                 )
         elif custom_llm_provider == "azure_text":
             # azure configs
-            api_type = get_secret("AZURE_API_TYPE") or "azure"
+            api_type = get_secret_str("AZURE_API_TYPE") or "azure"
 
-            api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE")
+            api_base = api_base or litellm.api_base or get_secret_str("AZURE_API_BASE")
+
+            if api_base is None:
+                raise ValueError(
+                    "api_base is required for Azure OpenAI LLM provider. Either set it dynamically or set the AZURE_API_BASE environment variable."
+                )
 
             api_version = (
-                api_version or litellm.api_version or get_secret("AZURE_API_VERSION")
+                api_version
+                or litellm.api_version
+                or get_secret_str("AZURE_API_VERSION")
             )
 
             api_key = (
                 api_key
                 or litellm.api_key
                 or litellm.azure_key
-                or get_secret("AZURE_OPENAI_API_KEY")
-                or get_secret("AZURE_API_KEY")
+                or get_secret_str("AZURE_OPENAI_API_KEY")
+                or get_secret_str("AZURE_API_KEY")
             )
 
             azure_ad_token = optional_params.get("extra_body", {}).pop(
                 "azure_ad_token", None
-            ) or get_secret("AZURE_AD_TOKEN")
+            ) or get_secret_str("AZURE_AD_TOKEN")
 
             azure_ad_token_provider = litellm_params.get(
                 "azure_ad_token_provider", None
@@ -1573,7 +1584,7 @@ def completion(  # type: ignore # noqa: PLR0915
                 headers=headers,
                 api_key=api_key,
                 api_base=api_base,
-                api_version=api_version,
+                api_version=cast(str, api_version),
                 api_type=api_type,
                 azure_ad_token=azure_ad_token,
                 azure_ad_token_provider=azure_ad_token_provider,
@@ -2545,15 +2556,10 @@ def completion(  # type: ignore # noqa: PLR0915
             )
         elif custom_llm_provider == "compactifai":
             api_key = (
-                api_key
-                or get_secret_str("COMPACTIFAI_API_KEY")
-                or litellm.api_key
+                api_key or get_secret_str("COMPACTIFAI_API_KEY") or litellm.api_key
             )
 
-            api_base = (
-                api_base
-                or "https://api.compactif.ai/v1"
-            )
+            api_base = api_base or "https://api.compactif.ai/v1"
 
             ## COMPLETION CALL
             response = base_llm_http_handler.completion(
@@ -2860,7 +2866,7 @@ def completion(  # type: ignore # noqa: PLR0915
                 logging_obj=logging,
                 acompletion=acompletion,
                 timeout=timeout,
-                custom_llm_provider=custom_llm_provider,
+                custom_llm_provider=custom_llm_provider,  # type: ignore
                 client=client,
                 api_base=api_base,
                 extra_headers=extra_headers,
@@ -2929,7 +2935,7 @@ def completion(  # type: ignore # noqa: PLR0915
                     logging_obj=logging,
                     acompletion=acompletion,
                     timeout=timeout,
-                    custom_llm_provider=custom_llm_provider,
+                    custom_llm_provider=custom_llm_provider,  # type: ignore
                     client=client,
                     api_base=api_base,
                     extra_headers=extra_headers,
@@ -3935,7 +3941,7 @@ def embedding(  # noqa: PLR0915
     litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj")  # type: ignore
     mock_response: Optional[List[float]] = kwargs.get("mock_response", None)  # type: ignore
     azure_ad_token_provider = kwargs.get("azure_ad_token_provider", None)
-    aembedding = kwargs.get("aembedding", None)
+    aembedding: Optional[bool] = kwargs.get("aembedding", None)
     extra_headers = kwargs.get("extra_headers", None)
     headers = kwargs.get("headers", None)
     ### CUSTOM MODEL COST ###
@@ -5615,7 +5621,7 @@ def speech(  # noqa: PLR0915
     if max_retries is None:
         max_retries = litellm.num_retries or openai.DEFAULT_MAX_RETRIES
     litellm_params_dict = get_litellm_params(**kwargs)
-    logging_obj = kwargs.get("litellm_logging_obj", None)
+    logging_obj: Logging = cast(Logging, kwargs.get("litellm_logging_obj"))
     logging_obj.update_environment_variables(
         model=model,
         user=user,
diff --git a/litellm/proxy/_experimental/mcp_server/cost_calculator.py b/litellm/proxy/_experimental/mcp_server/cost_calculator.py
index 3fe33b6dd5..b8fdba23d9 100644
--- a/litellm/proxy/_experimental/mcp_server/cost_calculator.py
+++ b/litellm/proxy/_experimental/mcp_server/cost_calculator.py
@@ -1,6 +1,7 @@
 """
 Cost calculator for MCP tools.
 """
+
 from typing import TYPE_CHECKING, Any, Optional, cast
 
 from litellm.types.mcp import MCPServerCostInfo
@@ -13,11 +14,12 @@ if TYPE_CHECKING:
 else:
     LitellmLoggingObject = Any
 
+
 class MCPCostCalculator:
     @staticmethod
     def calculate_mcp_tool_call_cost(
         litellm_logging_obj: Optional[LitellmLoggingObject],
-        ) -> float:
+    ) -> float:
         """
         Calculate the cost of an MCP tool call.
 
@@ -25,29 +27,43 @@ class MCPCostCalculator:
         """
         if litellm_logging_obj is None:
             return 0.0
-        
+
         #########################################################
         # Get the response cost from logging object model_call_details
         # This is set when a user modifies the response in a post_mcp_tool_call_hook
         #########################################################
-        response_cost = litellm_logging_obj.model_call_details.get("response_cost", None)
+        response_cost = litellm_logging_obj.model_call_details.get(
+            "response_cost", None
+        )
         if response_cost is not None:
             return response_cost
-        
+
         #########################################################
         # Unpack the mcp_tool_call_metadata
         #########################################################
-        mcp_tool_call_metadata: StandardLoggingMCPToolCall = cast(StandardLoggingMCPToolCall, litellm_logging_obj.model_call_details.get("mcp_tool_call_metadata", {})) or {}
-        mcp_server_cost_info_raw = mcp_tool_call_metadata.get("mcp_server_cost_info", {}) or {}
-        mcp_server_cost_info: MCPServerCostInfo = cast(MCPServerCostInfo, mcp_server_cost_info_raw)
+        mcp_tool_call_metadata: StandardLoggingMCPToolCall = (
+            cast(
+                StandardLoggingMCPToolCall,
+                litellm_logging_obj.model_call_details.get(
+                    "mcp_tool_call_metadata", {}
+                ),
+            )
+            or {}
+        )
+        mcp_server_cost_info: MCPServerCostInfo = (
+            mcp_tool_call_metadata.get("mcp_server_cost_info") or MCPServerCostInfo()
+        )
         #########################################################
         # User defined cost per query
         #########################################################
-        default_cost_per_query = mcp_server_cost_info.get("default_cost_per_query", None)
-        tool_name_to_cost_per_query: dict = mcp_server_cost_info.get("tool_name_to_cost_per_query", {}) or {}
+        default_cost_per_query = mcp_server_cost_info.get(
+            "default_cost_per_query", None
+        )
+        tool_name_to_cost_per_query: dict = (
+            mcp_server_cost_info.get("tool_name_to_cost_per_query", {}) or {}
+        )
         tool_name = mcp_tool_call_metadata.get("name", "")
 
-
         #########################################################
         # 1. If tool_name is in tool_name_to_cost_per_query, use the cost per query
         # 2. If tool_name is not in tool_name_to_cost_per_query, use the default cost per query
diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py
index 9c4ab70f0d..5e5099426a 100644
--- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py
+++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py
@@ -1,5 +1,5 @@
 import json
-from typing import Optional
+from typing import Optional, Tuple
 from urllib.parse import urlencode, urlparse, urlunparse
 
 from fastapi import APIRouter, Form, HTTPException, Request
@@ -36,7 +36,7 @@ def encode_state_with_base_url(base_url: str, original_state: str) -> str:
     return encrypted_state
 
 
-def decode_state_hash(encrypted_state: str) -> tuple[str, str]:
+def decode_state_hash(encrypted_state: str) -> Tuple[str, str]:
     """
     Decode an encrypted state to retrieve the base_url and original state.
 
diff --git a/litellm/proxy/_experimental/out/_next/static/0oPk2eYtSaTLaPyVixqA8/_buildManifest.js b/litellm/proxy/_experimental/out/_next/static/Ap4Kq4vtq74RgOyxD-zii/_buildManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/0oPk2eYtSaTLaPyVixqA8/_buildManifest.js
rename to litellm/proxy/_experimental/out/_next/static/Ap4Kq4vtq74RgOyxD-zii/_buildManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/0oPk2eYtSaTLaPyVixqA8/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/Ap4Kq4vtq74RgOyxD-zii/_ssgManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/0oPk2eYtSaTLaPyVixqA8/_ssgManifest.js
rename to litellm/proxy/_experimental/out/_next/static/Ap4Kq4vtq74RgOyxD-zii/_ssgManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/162-4e7640b4d68e1ae4.js b/litellm/proxy/_experimental/out/_next/static/chunks/162-dd6427ff1a4ad9f4.js
similarity index 50%
rename from litellm/proxy/_experimental/out/_next/static/chunks/162-4e7640b4d68e1ae4.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/162-dd6427ff1a4ad9f4.js
index e9bbcb3d1f..6960e05b16 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/162-4e7640b4d68e1ae4.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/162-dd6427ff1a4ad9f4.js
@@ -1 +1 @@
-"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[162],{36724:function(e,t,n){n.d(t,{Dx:function(){return i.Z},Zb:function(){return s.Z},xv:function(){return r.Z},zx:function(){return a.Z}});var a=n(20831),s=n(12514),r=n(84264),i=n(96761)},19130:function(e,t,n){n.d(t,{RM:function(){return s.Z},SC:function(){return l.Z},iA:function(){return a.Z},pj:function(){return r.Z},ss:function(){return i.Z},xs:function(){return o.Z}});var a=n(21626),s=n(97214),r=n(28241),i=n(58834),o=n(69552),l=n(71876)},88658:function(e,t,n){n.d(t,{L:function(){return s}});var a=n(49817);let s=e=>{let t;let{apiKeySource:n,accessToken:s,apiKey:r,inputMessage:i,chatHistory:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedMCPTools:m,endpointType:p,selectedModel:u,selectedSdk:g}=e,x="session"===n?s:r,h=window.location.origin,f=i||"Your prompt here",_=f.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),b=o.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),v={};l.length>0&&(v.tags=l),c.length>0&&(v.vector_stores=c),d.length>0&&(v.guardrails=d);let j=u||"your-model-name",y="azure"===g?'import openai\n\nclient = openai.AzureOpenAI(\n	api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n	azure_endpoint="').concat(h,'",\n	api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n	api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(h,'"\n)');switch(p){case a.KP.CHAT:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(j,'",\n    messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(j,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(_,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(j,'",\n    input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(j,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(_,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(j,'",\n	prompt="').concat(i,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(j,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(j,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(j,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;default:t="\n# Code generation for this endpoint is not implemented yet."}return"".concat(y,"\n").concat(t)}},49817:function(e,t,n){var a,s,r,i;n.d(t,{KP:function(){return s},vf:function(){return l}}),(r=a||(a={})).IMAGE_GENERATION="image_generation",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",(i=s||(s={})).IMAGE="image",i.CHAT="chat",i.RESPONSES="responses",i.IMAGE_EDITS="image_edits",i.ANTHROPIC_MESSAGES="anthropic_messages";let o={image_generation:"image",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages"},l=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let t=o[e];return console.log("endpointType:",t),t}return"chat"}},29488:function(e,t,n){n.d(t,{Hc:function(){return 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+"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[162],{36724:function(e,t,n){n.d(t,{Dx:function(){return i.Z},Zb:function(){return s.Z},xv:function(){return r.Z},zx:function(){return a.Z}});var a=n(20831),s=n(12514),r=n(84264),i=n(96761)},19130:function(e,t,n){n.d(t,{RM:function(){return s.Z},SC:function(){return l.Z},iA:function(){return a.Z},pj:function(){return r.Z},ss:function(){return i.Z},xs:function(){return o.Z}});var a=n(21626),s=n(97214),r=n(28241),i=n(58834),o=n(69552),l=n(71876)},88658:function(e,t,n){n.d(t,{L:function(){return s}});var a=n(49817);let s=e=>{let t;let{apiKeySource:n,accessToken:s,apiKey:r,inputMessage:i,chatHistory:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedMCPTools:m,endpointType:p,selectedModel:u,selectedSdk:g}=e,x="session"===n?s:r,h=window.location.origin,f=i||"Your prompt here",_=f.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),b=o.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),v={};l.length>0&&(v.tags=l),c.length>0&&(v.vector_stores=c),d.length>0&&(v.guardrails=d);let j=u||"your-model-name",y="azure"===g?'import openai\n\nclient = openai.AzureOpenAI(\n	api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n	azure_endpoint="').concat(h,'",\n	api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n	api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(h,'"\n)');switch(p){case a.KP.CHAT:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(j,'",\n    messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(j,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(_,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(j,'",\n    input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(j,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(_,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(j,'",\n	prompt="').concat(i,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(j,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(j,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(j,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;default:t="\n# Code generation for this endpoint is not implemented yet."}return"".concat(y,"\n").concat(t)}},49817:function(e,t,n){var a,s,r,i;n.d(t,{KP:function(){return s},vf:function(){return l}}),(r=a||(a={})).IMAGE_GENERATION="image_generation",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",(i=s||(s={})).IMAGE="image",i.CHAT="chat",i.RESPONSES="responses",i.IMAGE_EDITS="image_edits",i.ANTHROPIC_MESSAGES="anthropic_messages";let o={image_generation:"image",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages"},l=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let t=o[e];return console.log("endpointType:",t),t}return"chat"}},29488:function(e,t,n){n.d(t,{Hc:function(){return 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/220-89d73a525e307735.js b/litellm/proxy/_experimental/out/_next/static/chunks/313-0025fb08e386c4b8.js
similarity index 95%
rename from litellm/proxy/_experimental/out/_next/static/chunks/220-89d73a525e307735.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/313-0025fb08e386c4b8.js
index 57e9349d8f..93d7332457 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/220-89d73a525e307735.js
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y=ti(t,v,n,s,e.dragStartMousePosition,f,l,i,o,p),b=y.dropPosition,x=y.dropLevelOffset,w=y.dropTargetKey,k=y.dropContainerKey,S=y.dropTargetPos,E=y.dropAllowed,O=y.dragOverNodeKey;if(-1!==a.indexOf(w)||!E||(e.delayedDragEnterLogic||(e.delayedDragEnterLogic={}),Object.keys(e.delayedDragEnterLogic).forEach(function(t){clearTimeout(e.delayedDragEnterLogic[t])}),v.props.eventKey!==n.props.eventKey&&(t.persist(),e.delayedDragEnterLogic[m]=window.setTimeout(function(){if(null!==e.state.draggingNodeKey){var r=(0,es.Z)(o),a=i[n.props.eventKey];a&&(a.children||[]).length&&(r=tr(o,n.props.eventKey)),"expandedKeys"in e.props||e.setExpandedKeys(r),null==d||d(r,{node:e8(n.props),expanded:!0,nativeEvent:t.nativeEvent})}},800)),v.props.eventKey===w&&0===x)){e.resetDragState();return}e.setState({dragOverNodeKey:O,dropPosition:b,dropLevelOffset:x,dropTargetKey:w,dropContainerKey:k,dropTargetPos:S,dropAllowed:E}),null==u||u({event:t,node:e8(n.props),expandedKeys:o})},e.onNodeDragOver=function(t,n){var r=e.state,o=r.dragChildrenKeys,i=r.flattenNodes,a=r.keyEntities,l=r.expandedKeys,s=r.indent,c=e.props,u=c.onDragOver,d=c.allowDrop,f=c.direction,p=(0,eK.Z)(e).dragNode;if(p){var h=ti(t,p,n,s,e.dragStartMousePosition,d,i,a,l,f),m=h.dropPosition,g=h.dropLevelOffset,v=h.dropTargetKey,y=h.dropContainerKey,b=h.dropAllowed,x=h.dropTargetPos,w=h.dragOverNodeKey;-1===o.indexOf(v)&&b&&(p.props.eventKey===v&&0===g?null===e.state.dropPosition&&null===e.state.dropLevelOffset&&null===e.state.dropTargetKey&&null===e.state.dropContainerKey&&null===e.state.dropTargetPos&&!1===e.state.dropAllowed&&null===e.state.dragOverNodeKey||e.resetDragState():m===e.state.dropPosition&&g===e.state.dropLevelOffset&&v===e.state.dropTargetKey&&y===e.state.dropContainerKey&&x===e.state.dropTargetPos&&b===e.state.dropAllowed&&w===e.state.dragOverNodeKey||e.setState({dropPosition:m,dropLevelOffset:g,dropTargetKey:v,dropContainerKey:y,dropTargetPos:x,dropAllowed:b,dragOverNodeKey:w}),null==u||u({event:t,node:e8(n.props)}))}},e.onNodeDragLeave=function(t,n){e.currentMouseOverDroppableNodeKey!==n.props.eventKey||t.currentTarget.contains(t.relatedTarget)||(e.resetDragState(),e.currentMouseOverDroppableNodeKey=null);var r=e.props.onDragLeave;null==r||r({event:t,node:e8(n.props)})},e.onWindowDragEnd=function(t){e.onNodeDragEnd(t,null,!0),window.removeEventListener("dragend",e.onWindowDragEnd)},e.onNodeDragEnd=function(t,n){var r=e.props.onDragEnd;e.setState({dragOverNodeKey:null}),e.cleanDragState(),null==r||r({event:t,node:e8(n.props)}),e.dragNode=null,window.removeEventListener("dragend",e.onWindowDragEnd)},e.onNodeDrop=function(t,n){var r,o=arguments.length>2&&void 0!==arguments[2]&&arguments[2],i=e.state,a=i.dragChildrenKeys,l=i.dropPosition,s=i.dropTargetKey,c=i.dropTargetPos;if(i.dropAllowed){var u=e.props.onDrop;if(e.setState({dragOverNodeKey:null}),e.cleanDragState(),null!==s){var d=(0,O.Z)((0,O.Z)({},e3(s,e.getTreeNodeRequiredProps())),{},{active:(null===(r=e.getActiveItem())||void 0===r?void 0:r.key)===s,data:e.state.keyEntities[s].node}),f=-1!==a.indexOf(s);(0,M.ZP)(!f,"Can not drop to dragNode's children node. 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r.createElement(S.m,{tabIndex:0,role:"slider","aria-label":g,"aria-valuenow":e,className:"recharts-brush-traveller",onMouseEnter:this.handleEnterSlideOrTraveller,onMouseLeave:this.handleLeaveSlideOrTraveller,onMouseDown:this.travellerDragStartHandlers[t],onTouchStart:this.travellerDragStartHandlers[t],onKeyDown:function(e){["ArrowLeft","ArrowRight"].includes(e.key)&&(e.preventDefault(),e.stopPropagation(),n.handleTravellerMoveKeyboard("ArrowRight"===e.key?1:-1,t))},onFocus:function(){n.setState({isTravellerFocused:!0})},onBlur:function(){n.setState({isTravellerFocused:!1})},style:{cursor:"col-resize"}},a.renderTraveller(c,m))}},{key:"renderSlide",value:function(e,t){var n=this.props,o=n.y,i=n.height,a=n.stroke,l=n.travellerWidth;return 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y=ti(t,v,n,s,e.dragStartMousePosition,f,l,i,o,p),b=y.dropPosition,x=y.dropLevelOffset,w=y.dropTargetKey,k=y.dropContainerKey,S=y.dropTargetPos,E=y.dropAllowed,O=y.dragOverNodeKey;if(-1!==a.indexOf(w)||!E||(e.delayedDragEnterLogic||(e.delayedDragEnterLogic={}),Object.keys(e.delayedDragEnterLogic).forEach(function(t){clearTimeout(e.delayedDragEnterLogic[t])}),v.props.eventKey!==n.props.eventKey&&(t.persist(),e.delayedDragEnterLogic[m]=window.setTimeout(function(){if(null!==e.state.draggingNodeKey){var r=(0,es.Z)(o),a=i[n.props.eventKey];a&&(a.children||[]).length&&(r=tr(o,n.props.eventKey)),"expandedKeys"in e.props||e.setExpandedKeys(r),null==d||d(r,{node:e8(n.props),expanded:!0,nativeEvent:t.nativeEvent})}},800)),v.props.eventKey===w&&0===x)){e.resetDragState();return}e.setState({dragOverNodeKey:O,dropPosition:b,dropLevelOffset:x,dropTargetKey:w,dropContainerKey:k,dropTargetPos:S,dropAllowed:E}),null==u||u({event:t,node:e8(n.props),expandedKeys:o})},e.onNodeDragOver=function(t,n){var r=e.state,o=r.dragChildrenKeys,i=r.flattenNodes,a=r.keyEntities,l=r.expandedKeys,s=r.indent,c=e.props,u=c.onDragOver,d=c.allowDrop,f=c.direction,p=(0,eK.Z)(e).dragNode;if(p){var h=ti(t,p,n,s,e.dragStartMousePosition,d,i,a,l,f),m=h.dropPosition,g=h.dropLevelOffset,v=h.dropTargetKey,y=h.dropContainerKey,b=h.dropAllowed,x=h.dropTargetPos,w=h.dragOverNodeKey;-1===o.indexOf(v)&&b&&(p.props.eventKey===v&&0===g?null===e.state.dropPosition&&null===e.state.dropLevelOffset&&null===e.state.dropTargetKey&&null===e.state.dropContainerKey&&null===e.state.dropTargetPos&&!1===e.state.dropAllowed&&null===e.state.dragOverNodeKey||e.resetDragState():m===e.state.dropPosition&&g===e.state.dropLevelOffset&&v===e.state.dropTargetKey&&y===e.state.dropContainerKey&&x===e.state.dropTargetPos&&b===e.state.dropAllowed&&w===e.state.dragOverNodeKey||e.setState({dropPosition:m,dropLevelOffset:g,dropTargetKey:v,dropContainerKey:y,dropTargetPos:x,dropAllowed:b,dragOverNodeKey:w}),null==u||u({event:t,node:e8(n.props)}))}},e.onNodeDragLeave=function(t,n){e.currentMouseOverDroppableNodeKey!==n.props.eventKey||t.currentTarget.contains(t.relatedTarget)||(e.resetDragState(),e.currentMouseOverDroppableNodeKey=null);var r=e.props.onDragLeave;null==r||r({event:t,node:e8(n.props)})},e.onWindowDragEnd=function(t){e.onNodeDragEnd(t,null,!0),window.removeEventListener("dragend",e.onWindowDragEnd)},e.onNodeDragEnd=function(t,n){var r=e.props.onDragEnd;e.setState({dragOverNodeKey:null}),e.cleanDragState(),null==r||r({event:t,node:e8(n.props)}),e.dragNode=null,window.removeEventListener("dragend",e.onWindowDragEnd)},e.onNodeDrop=function(t,n){var r,o=arguments.length>2&&void 0!==arguments[2]&&arguments[2],i=e.state,a=i.dragChildrenKeys,l=i.dropPosition,s=i.dropTargetKey,c=i.dropTargetPos;if(i.dropAllowed){var u=e.props.onDrop;if(e.setState({dragOverNodeKey:null}),e.cleanDragState(),null!==s){var d=(0,O.Z)((0,O.Z)({},e6(s,e.getTreeNodeRequiredProps())),{},{active:(null===(r=e.getActiveItem())||void 0===r?void 0:r.key)===s,data:e.state.keyEntities[s].node}),f=-1!==a.indexOf(s);(0,M.ZP)(!f,"Can not drop to dragNode's children node. 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r.createElement(S.m,{tabIndex:0,role:"slider","aria-label":g,"aria-valuenow":e,className:"recharts-brush-traveller",onMouseEnter:this.handleEnterSlideOrTraveller,onMouseLeave:this.handleLeaveSlideOrTraveller,onMouseDown:this.travellerDragStartHandlers[t],onTouchStart:this.travellerDragStartHandlers[t],onKeyDown:function(e){["ArrowLeft","ArrowRight"].includes(e.key)&&(e.preventDefault(),e.stopPropagation(),n.handleTravellerMoveKeyboard("ArrowRight"===e.key?1:-1,t))},onFocus:function(){n.setState({isTravellerFocused:!0})},onBlur:function(){n.setState({isTravellerFocused:!1})},style:{cursor:"col-resize"}},a.renderTraveller(c,m))}},{key:"renderSlide",value:function(e,t){var n=this.props,o=n.y,i=n.height,a=n.stroke,l=n.travellerWidth;return 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not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tC;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,n){let r=new tC;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...n,headers:{...n?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},en(this,E,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,n){let r=n?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),en(this,l,"m",O).call(this);let{response:o,data:i}=await e.create({...t,stream:!0},{...n,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(o),i))en(this,l,"m",C).call(this,e);if(i.controller.signal?.aborted)throw new es;en(this,l,"m",j).call(this)}_connected(e){this.ended||(et(this,x,e,"f"),et(this,w,e?.headers.get("request-id"),"f"),en(this,u,"f").call(this,e),this._emit("connect"))}get ended(){return en(this,g,"f")}get errored(){return en(this,v,"f")}get aborted(){return en(this,y,"f")}abort(){this.controller.abort()}on(e,t){return(en(this,m,"f")[e]||(en(this,m,"f")[e]=[])).push({listener:t}),this}off(e,t){let n=en(this,m,"f")[e];if(!n)return this;let r=n.findIndex(e=>e.listener===t);return r>=0&&n.splice(r,1),this}once(e,t){return(en(this,m,"f")[e]||(en(this,m,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,n)=>{et(this,b,!0,"f"),"error"!==e&&this.once("error",n),this.once(e,t)})}async done(){et(this,b,!0,"f"),await en(this,f,"f")}get currentMessage(){return en(this,s,"f")}async finalMessage(){return await this.done(),en(this,l,"m",k).call(this)}async finalText(){return await this.done(),en(this,l,"m",S).call(this)}_emit(e,...t){if(en(this,g,"f"))return;"end"===e&&(et(this,g,!0,"f"),en(this,p,"f").call(this));let n=en(this,m,"f")[e];if(n&&(en(this,m,"f")[e]=n.filter(e=>!e.once),n.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];en(this,b,"f")||n?.length||Promise.reject(e),en(this,d,"f").call(this,e),en(this,h,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];en(this,b,"f")||n?.length||Promise.reject(e),en(this,d,"f").call(this,e),en(this,h,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",en(this,l,"m",k).call(this))}async _fromReadableStream(e,t){let n=t?.signal;n&&(n.aborted&&this.controller.abort(),n.addEventListener("abort",()=>this.controller.abort())),en(this,l,"m",O).call(this),this._connected(null);let r=eV.fromReadableStream(e,this.controller);for await(let e of r)en(this,l,"m",C).call(this,e);if(r.controller.signal?.aborted)throw new es;en(this,l,"m",j).call(this)}[(s=new WeakMap,c=new WeakMap,u=new WeakMap,d=new WeakMap,f=new WeakMap,p=new WeakMap,h=new WeakMap,m=new WeakMap,g=new WeakMap,v=new WeakMap,y=new WeakMap,b=new WeakMap,x=new WeakMap,w=new WeakMap,E=new WeakMap,l=new WeakSet,k=function(){if(0===this.receivedMessages.length)throw new ea("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},S=function(){if(0===this.receivedMessages.length)throw new ea("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new ea("stream ended without producing a content block with type=text");return e.join(" ")},O=function(){this.ended||et(this,s,void 0,"f")},C=function(e){if(this.ended)return;let t=en(this,l,"m",_).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let n=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===n.type&&this._emit("text",e.delta.text,n.text||"");break;case"citations_delta":"text"===n.type&&this._emit("citation",e.delta.citation,n.citations??[]);break;case"input_json_delta":tO(n)&&n.input&&this._emit("inputJson",e.delta.partial_json,n.input);break;case"thinking_delta":"thinking"===n.type&&this._emit("thinking",e.delta.thinking,n.thinking);break;case"signature_delta":"thinking"===n.type&&this._emit("signature",n.signature);break;default:e.delta}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":et(this,s,t,"f")}},j=function(){if(this.ended)throw new ea("stream has ended, this shouldn't happen");let e=en(this,s,"f");if(!e)throw new ea("request ended without sending any chunks");return et(this,s,void 0,"f"),e},_=function(e){let t=en(this,s,"f");if("message_start"===e.type){if(t)throw new ea(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new ea(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tO(n)){let t=n[tE]||"";if(Object.defineProperty(n,tE,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{n.input=tS(t)}catch(n){let e=new ea(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${n}. JSON: ${t}`);en(this,E,"f").call(this,e)}}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eV(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}let tj={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},t_={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tP extends tc{constructor(){super(...arguments),this.batches=new ty(this._client)}create(e,t){let{betas:n,...r}=e;r.model in t_&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${t_[r.model]}
 Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let o=this._client._options.timeout;if(!r.stream&&null==o){let e=tj[r.model]??void 0;o=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:o??6e5,...t,headers:tf([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return tC.createMessage(this,e,t)}countTokens(e,t){let{betas:n,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:tf([{"anthropic-beta":[...n??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tP.Batches=ty;class tT extends tc{constructor(){super(...arguments),this.models=new tg(this._client),this.messages=new tP(this._client),this.files=new tm(this._client)}}tT.Models=tg,tT.Messages=tP,tT.Files=tm;class tM extends tc{create(e,t){let{betas:n,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:tf([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tN="__json_buf";function tA(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tI{constructor(){P.add(this),this.messages=[],this.receivedMessages=[],T.set(this,void 0),this.controller=new AbortController,M.set(this,void 0),N.set(this,()=>{}),A.set(this,()=>{}),I.set(this,void 0),R.set(this,()=>{}),D.set(this,()=>{}),L.set(this,{}),z.set(this,!1),Z.set(this,!1),B.set(this,!1),F.set(this,!1),H.set(this,void 0),q.set(this,void 0),K.set(this,e=>{if(et(this,Z,!0,"f"),eo(e)&&(e=new es),e instanceof es)return et(this,B,!0,"f"),this._emit("abort",e);if(e instanceof ea)return this._emit("error",e);if(e instanceof Error){let t=new ea(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new ea(String(e)))}),et(this,M,new Promise((e,t)=>{et(this,N,e,"f"),et(this,A,t,"f")}),"f"),et(this,I,new Promise((e,t)=>{et(this,R,e,"f"),et(this,D,t,"f")}),"f"),en(this,M,"f").catch(()=>{}),en(this,I,"f").catch(()=>{})}get response(){return en(this,H,"f")}get request_id(){return en(this,q,"f")}async withResponse(){let e=await en(this,M,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tI;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,n){let r=new tI;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...n,headers:{...n?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},en(this,K,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,n){let r=n?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),en(this,P,"m",V).call(this);let{response:o,data:i}=await e.create({...t,stream:!0},{...n,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(o),i))en(this,P,"m",$).call(this,e);if(i.controller.signal?.aborted)throw new es;en(this,P,"m",X).call(this)}_connected(e){this.ended||(et(this,H,e,"f"),et(this,q,e?.headers.get("request-id"),"f"),en(this,N,"f").call(this,e),this._emit("connect"))}get ended(){return en(this,z,"f")}get errored(){return en(this,Z,"f")}get aborted(){return en(this,B,"f")}abort(){this.controller.abort()}on(e,t){return(en(this,L,"f")[e]||(en(this,L,"f")[e]=[])).push({listener:t}),this}off(e,t){let n=en(this,L,"f")[e];if(!n)return this;let r=n.findIndex(e=>e.listener===t);return r>=0&&n.splice(r,1),this}once(e,t){return(en(this,L,"f")[e]||(en(this,L,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,n)=>{et(this,F,!0,"f"),"error"!==e&&this.once("error",n),this.once(e,t)})}async done(){et(this,F,!0,"f"),await en(this,I,"f")}get currentMessage(){return en(this,T,"f")}async finalMessage(){return await this.done(),en(this,P,"m",U).call(this)}async finalText(){return await this.done(),en(this,P,"m",W).call(this)}_emit(e,...t){if(en(this,z,"f"))return;"end"===e&&(et(this,z,!0,"f"),en(this,R,"f").call(this));let n=en(this,L,"f")[e];if(n&&(en(this,L,"f")[e]=n.filter(e=>!e.once),n.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];en(this,F,"f")||n?.length||Promise.reject(e),en(this,A,"f").call(this,e),en(this,D,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];en(this,F,"f")||n?.length||Promise.reject(e),en(this,A,"f").call(this,e),en(this,D,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",en(this,P,"m",U).call(this))}async _fromReadableStream(e,t){let n=t?.signal;n&&(n.aborted&&this.controller.abort(),n.addEventListener("abort",()=>this.controller.abort())),en(this,P,"m",V).call(this),this._connected(null);let r=eV.fromReadableStream(e,this.controller);for await(let e of r)en(this,P,"m",$).call(this,e);if(r.controller.signal?.aborted)throw new es;en(this,P,"m",X).call(this)}[(T=new WeakMap,M=new WeakMap,N=new WeakMap,A=new WeakMap,I=new WeakMap,R=new WeakMap,D=new WeakMap,L=new WeakMap,z=new WeakMap,Z=new WeakMap,B=new WeakMap,F=new WeakMap,H=new WeakMap,q=new WeakMap,K=new WeakMap,P=new WeakSet,U=function(){if(0===this.receivedMessages.length)throw new ea("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},W=function(){if(0===this.receivedMessages.length)throw new ea("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new ea("stream ended without producing a content block with type=text");return e.join(" ")},V=function(){this.ended||et(this,T,void 0,"f")},$=function(e){if(this.ended)return;let t=en(this,P,"m",G).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let n=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===n.type&&this._emit("text",e.delta.text,n.text||"");break;case"citations_delta":"text"===n.type&&this._emit("citation",e.delta.citation,n.citations??[]);break;case"input_json_delta":tA(n)&&n.input&&this._emit("inputJson",e.delta.partial_json,n.input);break;case"thinking_delta":"thinking"===n.type&&this._emit("thinking",e.delta.thinking,n.thinking);break;case"signature_delta":"thinking"===n.type&&this._emit("signature",n.signature);break;default:e.delta}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":et(this,T,t,"f")}},X=function(){if(this.ended)throw new ea("stream has ended, this shouldn't happen");let e=en(this,T,"f");if(!e)throw new ea("request ended without sending any chunks");return et(this,T,void 0,"f"),e},G=function(e){let t=en(this,T,"f");if("message_start"===e.type){if(t)throw new ea(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new ea(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tA(n)){let t=n[tN]||"";Object.defineProperty(n,tN,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(n.input=tS(t))}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eV(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}class tR extends tc{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(th`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",e2,{query:e,...t})}delete(e,t){return this._client.delete(th`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(th`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let n=await this.retrieve(e);if(!n.results_url)throw new ea(`No batch \`results_url\`; Has it finished processing? ${n.processing_status} - ${n.id}`);return this._client.get(n.results_url,{...t,headers:tf([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tv.fromResponse(t.response,t.controller))}}class tD extends tc{constructor(){super(...arguments),this.batches=new tR(this._client)}create(e,t){e.model in tL&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tL[e.model]}
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I({message:`Giving up on waiting for file ${e} to finish processing after ${n} milliseconds.`});return i}}class t5 extends t0{}t8.FileObjectsPage=t5;class t7 extends tG{createVariation(e,t){return this._client.post("/images/variations",td({body:e,...t}))}edit(e,t){return this._client.post("/images/edits",td({body:e,...t}))}generate(e,t){return this._client.post("/images/generations",{body:e,...t})}}class t9 extends tG{create(e,t){return this._client.post("/audio/speech",{body:e,...t,headers:{Accept:"application/octet-stream",...t?.headers},__binaryResponse:!0})}}class ne extends tG{create(e,t){return this._client.post("/audio/transcriptions",td({body:e,...t,stream:e.stream??!1,__metadata:{model:e.model}}))}}class nt extends tG{create(e,t){return this._client.post("/audio/translations",td({body:e,...t,__metadata:{model:e.model}}))}}class nn extends tG{constructor(){super(...arguments),this.transcriptions=new ne(this._client),this.translations=new nt(this._client),this.speech=new t9(this._client)}}nn.Transcriptions=ne,nn.Translations=nt,nn.Speech=t9;class nr extends tG{create(e,t){return this._client.post("/moderations",{body:e,...t})}}class no extends tG{retrieve(e,t){return this._client.get(`/models/${e}`,t)}list(e){return this._client.getAPIList("/models",ni,e)}del(e,t){return this._client.delete(`/models/${e}`,t)}}class ni extends tJ{}no.ModelsPage=ni;class na extends tG{create(e,t,n){return this._client.getAPIList(`/fine_tuning/checkpoints/${e}/permissions`,nl,{body:t,method:"post",...n})}retrieve(e,t={},n){return tj(t)?this.retrieve(e,{},t):this._client.get(`/fine_tuning/checkpoints/${e}/permissions`,{query:t,...n})}del(e,t){return this._client.delete(`/fine_tuning/checkpoints/${e}/permissions`,t)}}class nl extends tJ{}na.PermissionCreateResponsesPage=nl;class ns extends tG{constructor(){super(...arguments),this.permissions=new na(this._client)}}ns.Permissions=na,ns.PermissionCreateResponsesPage=nl;class nc extends tG{list(e,t={},n){return 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ns(this._client)}}nh.Jobs=nd,nh.FineTuningJobsPage=nf,nh.FineTuningJobEventsPage=np,nh.Checkpoints=ns;let nm=async e=>{let t=await Promise.allSettled(e),n=t.filter(e=>"rejected"===e.status);if(n.length){for(let e of n)console.error(e.reason);throw Error(`${n.length} promise(s) failed - see the above errors`)}let r=[];for(let e of t)"fulfilled"===e.status&&r.push(e.value);return r};class ng extends tG{create(e,t,n){return this._client.post(`/vector_stores/${e}/files`,{body:t,...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}retrieve(e,t,n){return this._client.get(`/vector_stores/${e}/files/${t}`,{...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}update(e,t,n,r){return this._client.post(`/vector_stores/${e}/files/${t}`,{body:n,...r,headers:{"OpenAI-Beta":"assistants=v2",...r?.headers}})}list(e,t={},n){return 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uploadAndPoll(e,t,n){let r=await this.upload(e,t,n);return await this.poll(e,r.id,n)}content(e,t,n){return this._client.getAPIList(`/vector_stores/${e}/files/${t}/content`,ny,{...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}}class nv extends t0{}class ny extends tJ{}ng.VectorStoreFilesPage=nv,ng.FileContentResponsesPage=ny;class nb extends tG{create(e,t,n){return this._client.post(`/vector_stores/${e}/file_batches`,{body:t,...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}retrieve(e,t,n){return this._client.get(`/vector_stores/${e}/file_batches/${t}`,{...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}cancel(e,t,n){return this._client.post(`/vector_stores/${e}/file_batches/${t}/cancel`,{...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}async createAndPoll(e,t,n){let r=await this.create(e,t);return await this.poll(e,r.id,n)}listFiles(e,t,n={},r){return tj(n)?this.listFiles(e,t,{},n):this._client.getAPIList(`/vector_stores/${e}/file_batches/${t}/files`,nv,{query:n,...r,headers:{"OpenAI-Beta":"assistants=v2",...r?.headers}})}async poll(e,t,n){let r={...n?.headers,"X-Stainless-Poll-Helper":"true"};for(n?.pollIntervalMs&&(r["X-Stainless-Custom-Poll-Interval"]=n.pollIntervalMs.toString());;){let{data:o,response:i}=await this.retrieve(e,t,{...n,headers:r}).withResponse();switch(o.status){case"in_progress":let a=5e3;if(n?.pollIntervalMs)a=n.pollIntervalMs;else{let e=i.headers.get("openai-poll-after-ms");if(e){let t=parseInt(e);isNaN(t)||(a=t)}}await tR(a);break;case"failed":case"cancelled":case"completed":return o}}}async uploadAndPoll(e,{files:t,fileIds:n=[]},r){if(null==t||0==t.length)throw Error("No `files` provided to process. If you've already uploaded files you should use `.createAndPoll()` instead");let o=Math.min(r?.maxConcurrency??5,t.length),i=this._client,a=t.values(),l=[...n];async function s(e){for(let t of e){let e=await i.files.create({file:t,purpose:"assistants"},r);l.push(e.id)}}let c=Array(o).fill(a).map(s);return await nm(c),await this.createAndPoll(e,{file_ids:l})}}class nx extends tG{constructor(){super(...arguments),this.files=new ng(this._client),this.fileBatches=new nb(this._client)}create(e,t){return this._client.post("/vector_stores",{body:e,...t,headers:{"OpenAI-Beta":"assistants=v2",...t?.headers}})}retrieve(e,t){return this._client.get(`/vector_stores/${e}`,{...t,headers:{"OpenAI-Beta":"assistants=v2",...t?.headers}})}update(e,t,n){return this._client.post(`/vector_stores/${e}`,{body:t,...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}list(e={},t){return 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this.done(),nD(this,ea,"m",es).call(this)}async finalFunctionCall(){return await this.done(),nD(this,ea,"m",ec).call(this)}async finalFunctionCallResult(){return await this.done(),nD(this,ea,"m",eu).call(this)}async totalUsage(){return await this.done(),nD(this,ea,"m",ed).call(this)}allChatCompletions(){return[...this._chatCompletions]}_emitFinal(){let e=this._chatCompletions[this._chatCompletions.length-1];e&&this._emit("finalChatCompletion",e);let t=nD(this,ea,"m",es).call(this);t&&this._emit("finalMessage",t);let n=nD(this,ea,"m",el).call(this);n&&this._emit("finalContent",n);let r=nD(this,ea,"m",ec).call(this);r&&this._emit("finalFunctionCall",r);let o=nD(this,ea,"m",eu).call(this);null!=o&&this._emit("finalFunctionCallResult",o),this._chatCompletions.some(e=>e.usage)&&this._emit("totalUsage",nD(this,ea,"m",ed).call(this))}async _createChatCompletion(e,t,n){let 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nL{constructor(e){super(),eh.add(this),em.set(this,void 0),eg.set(this,void 0),ev.set(this,void 0),nU(this,em,e,"f"),nU(this,eg,[],"f")}get currentChatCompletionSnapshot(){return nW(this,ev,"f")}static fromReadableStream(e){let t=new nK(null);return t._run(()=>t._fromReadableStream(e)),t}static createChatCompletion(e,t,n){let r=new nK(t);return r._run(()=>r._runChatCompletion(e,{...t,stream:!0},{...n,headers:{...n?.headers,"X-Stainless-Helper-Method":"stream"}})),r}async _createChatCompletion(e,t,n){super._createChatCompletion;let r=n?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),nW(this,eh,"m",ey).call(this);let o=await e.chat.completions.create({...t,stream:!0},{...n,signal:this.controller.signal});for await(let e of(this._connected(),o))nW(this,eh,"m",ex).call(this,e);if(o.controller.signal?.aborted)throw new N;return this._addChatCompletion(nW(this,eh,"m",eS).call(this))}async _fromReadableStream(e,t){let n;let r=t?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),nW(this,eh,"m",ey).call(this),this._connected();let o=e5.fromReadableStream(e,this.controller);for await(let e of o)n&&n!==e.id&&this._addChatCompletion(nW(this,eh,"m",eS).call(this)),nW(this,eh,"m",ex).call(this,e),n=e.id;if(o.controller.signal?.aborted)throw new N;return this._addChatCompletion(nW(this,eh,"m",eS).call(this))}[(em=new WeakMap,eg=new WeakMap,ev=new WeakMap,eh=new WeakSet,ey=function(){this.ended||nU(this,ev,void 0,"f")},eb=function(e){let t=nW(this,eg,"f")[e.index];return t||(t={content_done:!1,refusal_done:!1,logprobs_content_done:!1,logprobs_refusal_done:!1,done_tool_calls:new Set,current_tool_call_index:null},nW(this,eg,"f")[e.index]=t),t},ex=function(e){if(this.ended)return;let t=nW(this,eh,"m",eO).call(this,e);for(let n of(this._emit("chunk",e,t),e.choices)){let e=t.choices[n.index];null!=n.delta.content&&e.message?.role==="assistant"&&e.message?.content&&(this._emit("content",n.delta.content,e.message.content),this._emit("content.delta",{delta:n.delta.content,snapshot:e.message.content,parsed:e.message.parsed})),null!=n.delta.refusal&&e.message?.role==="assistant"&&e.message?.refusal&&this._emit("refusal.delta",{delta:n.delta.refusal,snapshot:e.message.refusal}),n.logprobs?.content!=null&&e.message?.role==="assistant"&&this._emit("logprobs.content.delta",{content:n.logprobs?.content,snapshot:e.logprobs?.content??[]}),n.logprobs?.refusal!=null&&e.message?.role==="assistant"&&this._emit("logprobs.refusal.delta",{refusal:n.logprobs?.refusal,snapshot:e.logprobs?.refusal??[]});let r=nW(this,eh,"m",eb).call(this,e);for(let t of(e.finish_reason&&(nW(this,eh,"m",ek).call(this,e),null!=r.current_tool_call_index&&nW(this,eh,"m",ew).call(this,e,r.current_tool_call_index)),n.delta.tool_calls??[]))r.current_tool_call_index!==t.index&&(nW(this,eh,"m",ek).call(this,e),null!=r.current_tool_call_index&&nW(this,eh,"m",ew).call(this,e,r.current_tool_call_index)),r.current_tool_call_index=t.index;for(let t of n.delta.tool_calls??[]){let n=e.message.tool_calls?.[t.index];n?.type&&(n?.type==="function"?this._emit("tool_calls.function.arguments.delta",{name:n.function?.name,index:t.index,arguments:n.function.arguments,parsed_arguments:n.function.parsed_arguments,arguments_delta:t.function?.arguments??""}):n?.type)}}},ew=function(e,t){if(nW(this,eh,"m",eb).call(this,e).done_tool_calls.has(t))return;let n=e.message.tool_calls?.[t];if(!n)throw Error("no tool call snapshot");if(!n.type)throw Error("tool call snapshot missing `type`");if("function"===n.type){let e=nW(this,em,"f")?.tools?.find(e=>"function"===e.type&&e.function.name===n.function.name);this._emit("tool_calls.function.arguments.done",{name:n.function.name,index:t,arguments:n.function.arguments,parsed_arguments:nA(e)?e.$parseRaw(n.function.arguments):e?.function.strict?JSON.parse(n.function.arguments):null})}else n.type},ek=function(e){let t=nW(this,eh,"m",eb).call(this,e);if(e.message.content&&!t.content_done){t.content_done=!0;let n=nW(this,eh,"m",eE).call(this);this._emit("content.done",{content:e.message.content,parsed:n?n.$parseRaw(e.message.content):null})}e.message.refusal&&!t.refusal_done&&(t.refusal_done=!0,this._emit("refusal.done",{refusal:e.message.refusal})),e.logprobs?.content&&!t.logprobs_content_done&&(t.logprobs_content_done=!0,this._emit("logprobs.content.done",{content:e.logprobs.content})),e.logprobs?.refusal&&!t.logprobs_refusal_done&&(t.logprobs_refusal_done=!0,this._emit("logprobs.refusal.done",{refusal:e.logprobs.refusal}))},eS=function(){if(this.ended)throw new T("stream has ended, this shouldn't happen");let e=nW(this,ev,"f");if(!e)throw new T("request ended without sending any chunks");return nU(this,ev,void 0,"f"),nU(this,eg,[],"f"),function(e,t){var n;let{id:r,choices:o,created:i,model:a,system_fingerprint:l,...s}=e;return n={...s,id:r,choices:o.map(({message:t,finish_reason:n,index:r,logprobs:o,...i})=>{if(!n)throw new T(`missing finish_reason for choice 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Only strict function tools can be auto-parsed`)}}(e.tools),this._client.chat.completions.create(e,{...t,headers:{...t?.headers,"X-Stainless-Helper-Method":"beta.chat.completions.parse"}})._thenUnwrap(t=>nI(t,e))}runFunctions(e,t){return e.stream?n$.runFunctions(this._client,e,t):nz.runFunctions(this._client,e,t)}runTools(e,t){return e.stream?n$.runTools(this._client,e,t):nz.runTools(this._client,e,t)}stream(e,t){return nK.createChatCompletion(this._client,e,t)}}class nG extends tG{constructor(){super(...arguments),this.completions=new nX(this._client)}}(nG||(nG={})).Completions=nX;class nY extends tG{create(e,t){return this._client.post("/realtime/sessions",{body:e,...t,headers:{"OpenAI-Beta":"assistants=v2",...t?.headers}})}}class nQ extends tG{create(e,t){return this._client.post("/realtime/transcription_sessions",{body:e,...t,headers:{"OpenAI-Beta":"assistants=v2",...t?.headers}})}}class nJ extends tG{constructor(){super(...arguments),this.sessions=new 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this._addRun(n0(this,eC,"m",eB).call(this))}static createThreadAssistantStream(e,t,n){let r=new n2;return r._run(()=>r._threadAssistantStream(e,t,{...n,headers:{...n?.headers,"X-Stainless-Helper-Method":"stream"}})),r}static createAssistantStream(e,t,n,r){let o=new n2;return o._run(()=>o._runAssistantStream(e,t,n,{...r,headers:{...r?.headers,"X-Stainless-Helper-Method":"stream"}})),o}currentEvent(){return n0(this,eD,"f")}currentRun(){return n0(this,eL,"f")}currentMessageSnapshot(){return n0(this,eT,"f")}currentRunStepSnapshot(){return n0(this,ez,"f")}async finalRunSteps(){return await this.done(),Object.values(n0(this,e_,"f"))}async finalMessages(){return await this.done(),Object.values(n0(this,eP,"f"))}async finalRun(){if(await this.done(),!n0(this,eM,"f"))throw Error("Final run was not received.");return n0(this,eM,"f")}async _createThreadAssistantStream(e,t,n){let r=n?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort()));let o={...t,stream:!0},i=await e.createAndRun(o,{...n,signal:this.controller.signal});for await(let e of(this._connected(),i))n0(this,eC,"m",eZ).call(this,e);if(i.controller.signal?.aborted)throw new N;return this._addRun(n0(this,eC,"m",eB).call(this))}async _createAssistantStream(e,t,n,r){let o=r?.signal;o&&(o.aborted&&this.controller.abort(),o.addEventListener("abort",()=>this.controller.abort()));let i={...n,stream:!0},a=await e.create(t,i,{...r,signal:this.controller.signal});for await(let e of(this._connected(),a))n0(this,eC,"m",eZ).call(this,e);if(a.controller.signal?.aborted)throw new N;return this._addRun(n0(this,eC,"m",eB).call(this))}static accumulateDelta(e,t){for(let[n,r]of Object.entries(t)){if(!e.hasOwnProperty(n)){e[n]=r;continue}let t=e[n];if(null==t||"index"===n||"type"===n){e[n]=r;continue}if("string"==typeof t&&"string"==typeof r)t+=r;else if("number"==typeof t&&"number"==typeof r)t+=r;else if(tX(t)&&tX(r))t=this.accumulateDelta(t,r);else 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should be processed earlier")}},eB=function(){if(this.ended)throw new T("stream has ended, this shouldn't happen");if(!n0(this,eM,"f"))throw Error("Final run has not been received");return n0(this,eM,"f")},eF=function(e){let[t,n]=n0(this,eC,"m",eW).call(this,e,n0(this,eT,"f"));for(let e of(n1(this,eT,t,"f"),n0(this,eP,"f")[t.id]=t,n)){let n=t.content[e.index];n?.type=="text"&&this._emit("textCreated",n.text)}switch(e.event){case"thread.message.created":this._emit("messageCreated",e.data);break;case"thread.message.in_progress":break;case"thread.message.delta":if(this._emit("messageDelta",e.data.delta,t),e.data.delta.content)for(let n of e.data.delta.content){if("text"==n.type&&n.text){let e=n.text,r=t.content[n.index];if(r&&"text"==r.type)this._emit("textDelta",e,r.text);else throw Error("The snapshot associated with this text delta is not text or missing")}if(n.index!=n0(this,eN,"f")){if(n0(this,eA,"f"))switch(n0(this,eA,"f").type){case"text":this._emit("textDone",n0(this,eA,"f").text,n0(this,eT,"f"));break;case"image_file":this._emit("imageFileDone",n0(this,eA,"f").image_file,n0(this,eT,"f"))}n1(this,eN,n.index,"f")}n1(this,eA,t.content[n.index],"f")}break;case"thread.message.completed":case"thread.message.incomplete":if(void 0!==n0(this,eN,"f")){let t=e.data.content[n0(this,eN,"f")];if(t)switch(t.type){case"image_file":this._emit("imageFileDone",t.image_file,n0(this,eT,"f"));break;case"text":this._emit("textDone",t.text,n0(this,eT,"f"))}}n0(this,eT,"f")&&this._emit("messageDone",e.data),n1(this,eT,void 0,"f")}},eH=function(e){let t=n0(this,eC,"m",eU).call(this,e);switch(n1(this,ez,t,"f"),e.event){case"thread.run.step.created":this._emit("runStepCreated",e.data);break;case"thread.run.step.delta":let n=e.data.delta;if(n.step_details&&"tool_calls"==n.step_details.type&&n.step_details.tool_calls&&"tool_calls"==t.step_details.type)for(let e of n.step_details.tool_calls)e.index==n0(this,eI,"f")?this._emit("toolCallDelta",e,t.step_details.tool_calls[e.index]):(n0(this,eR,"f")&&this._emit("toolCallDone",n0(this,eR,"f")),n1(this,eI,e.index,"f"),n1(this,eR,t.step_details.tool_calls[e.index],"f"),n0(this,eR,"f")&&this._emit("toolCallCreated",n0(this,eR,"f")));this._emit("runStepDelta",e.data.delta,t);break;case"thread.run.step.completed":case"thread.run.step.failed":case"thread.run.step.cancelled":case"thread.run.step.expired":n1(this,ez,void 0,"f"),"tool_calls"==e.data.step_details.type&&n0(this,eR,"f")&&(this._emit("toolCallDone",n0(this,eR,"f")),n1(this,eR,void 0,"f")),this._emit("runStepDone",e.data,t)}},eq=function(e){n0(this,ej,"f").push(e),this._emit("event",e)},eU=function(e){switch(e.event){case"thread.run.step.created":return n0(this,e_,"f")[e.data.id]=e.data,e.data;case"thread.run.step.delta":let t=n0(this,e_,"f")[e.data.id];if(!t)throw Error("Received a RunStepDelta before creation of a snapshot");let n=e.data;if(n.delta){let r=n2.accumulateDelta(t,n.delta);n0(this,e_,"f")[e.data.id]=r}return n0(this,e_,"f")[e.data.id];case"thread.run.step.completed":case"thread.run.step.failed":case"thread.run.step.cancelled":case"thread.run.step.expired":case"thread.run.step.in_progress":n0(this,e_,"f")[e.data.id]=e.data}if(n0(this,e_,"f")[e.data.id])return n0(this,e_,"f")[e.data.id];throw Error("No snapshot available")},eW=function(e,t){let n=[];switch(e.event){case"thread.message.created":return[e.data,n];case"thread.message.delta":if(!t)throw Error("Received a delta with no existing snapshot (there should be one from message creation)");let r=e.data;if(r.delta.content)for(let e of r.delta.content)if(e.index in t.content){let n=t.content[e.index];t.content[e.index]=n0(this,eC,"m",eK).call(this,e,n)}else t.content[e.index]=e,n.push(e);return[t,n];case"thread.message.in_progress":case"thread.message.completed":case"thread.message.incomplete":if(t)return[t,n];throw Error("Received thread message event with no existing snapshot")}throw Error("Tried to accumulate a non-message event")},eK=function(e,t){return n2.accumulateDelta(t,e)},eV=function(e){switch(n1(this,eL,e.data,"f"),e.event){case"thread.run.created":case"thread.run.queued":case"thread.run.in_progress":break;case"thread.run.requires_action":case"thread.run.cancelled":case"thread.run.failed":case"thread.run.completed":case"thread.run.expired":n1(this,eM,e.data,"f"),n0(this,eR,"f")&&(this._emit("toolCallDone",n0(this,eR,"f")),n1(this,eR,void 0,"f"))}};class n4 extends tG{create(e,t,n){return this._client.post(`/threads/${e}/messages`,{body:t,...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}retrieve(e,t,n){return this._client.get(`/threads/${e}/messages/${t}`,{...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}update(e,t,n,r){return this._client.post(`/threads/${e}/messages/${t}`,{body:n,...r,headers:{"OpenAI-Beta":"assistants=v2",...r?.headers}})}list(e,t={},n){return tj(t)?this.list(e,{},t):this._client.getAPIList(`/threads/${e}/messages`,n6,{query:t,...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}del(e,t,n){return this._client.delete(`/threads/${e}/messages/${t}`,{...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}}class n6 extends t0{}n4.MessagesPage=n6;class n3 extends tG{retrieve(e,t,n,r={},o){return tj(r)?this.retrieve(e,t,n,{},r):this._client.get(`/threads/${e}/runs/${t}/steps/${n}`,{query:r,...o,headers:{"OpenAI-Beta":"assistants=v2",...o?.headers}})}list(e,t,n={},r){return tj(n)?this.list(e,t,{},n):this._client.getAPIList(`/threads/${e}/runs/${t}/steps`,n8,{query:n,...r,headers:{"OpenAI-Beta":"assistants=v2",...r?.headers}})}}class n8 extends t0{}n3.RunStepsPage=n8;class n5 extends tG{constructor(){super(...arguments),this.steps=new n3(this._client)}create(e,t,n){let{include:r,...o}=t;return this._client.post(`/threads/${e}/runs`,{query:{include:r},body:o,...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers},stream:t.stream??!1})}retrieve(e,t,n){return this._client.get(`/threads/${e}/runs/${t}`,{...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}update(e,t,n,r){return this._client.post(`/threads/${e}/runs/${t}`,{body:n,...r,headers:{"OpenAI-Beta":"assistants=v2",...r?.headers}})}list(e,t={},n){return tj(t)?this.list(e,{},t):this._client.getAPIList(`/threads/${e}/runs`,n7,{query:t,...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}cancel(e,t,n){return this._client.post(`/threads/${e}/runs/${t}/cancel`,{...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}async createAndPoll(e,t,n){let r=await this.create(e,t,n);return await this.poll(e,r.id,n)}createAndStream(e,t,n){return 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n0(this,e_,"f")[e.data.id]=e.data,e.data;case"thread.run.step.delta":let t=n0(this,e_,"f")[e.data.id];if(!t)throw Error("Received a RunStepDelta before creation of a snapshot");let n=e.data;if(n.delta){let r=n2.accumulateDelta(t,n.delta);n0(this,e_,"f")[e.data.id]=r}return n0(this,e_,"f")[e.data.id];case"thread.run.step.completed":case"thread.run.step.failed":case"thread.run.step.cancelled":case"thread.run.step.expired":case"thread.run.step.in_progress":n0(this,e_,"f")[e.data.id]=e.data}if(n0(this,e_,"f")[e.data.id])return n0(this,e_,"f")[e.data.id];throw Error("No snapshot available")},eW=function(e,t){let n=[];switch(e.event){case"thread.message.created":return[e.data,n];case"thread.message.delta":if(!t)throw Error("Received a delta with no existing snapshot (there should be one from message creation)");let r=e.data;if(r.delta.content)for(let e of r.delta.content)if(e.index in t.content){let n=t.content[e.index];t.content[e.index]=n0(this,eC,"m",eK).call(this,e,n)}else t.content[e.index]=e,n.push(e);return[t,n];case"thread.message.in_progress":case"thread.message.completed":case"thread.message.incomplete":if(t)return[t,n];throw Error("Received thread message event with no existing snapshot")}throw Error("Tried to accumulate a non-message event")},eK=function(e,t){return n2.accumulateDelta(t,e)},eV=function(e){switch(n1(this,eL,e.data,"f"),e.event){case"thread.run.created":case"thread.run.queued":case"thread.run.in_progress":break;case"thread.run.requires_action":case"thread.run.cancelled":case"thread.run.failed":case"thread.run.completed":case"thread.run.expired":n1(this,eM,e.data,"f"),n0(this,eR,"f")&&(this._emit("toolCallDone",n0(this,eR,"f")),n1(this,eR,void 0,"f"))}};class n4 extends tG{create(e,t,n){return this._client.post(`/threads/${e}/messages`,{body:t,...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}retrieve(e,t,n){return this._client.get(`/threads/${e}/messages/${t}`,{...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}update(e,t,n,r){return this._client.post(`/threads/${e}/messages/${t}`,{body:n,...r,headers:{"OpenAI-Beta":"assistants=v2",...r?.headers}})}list(e,t={},n){return tj(t)?this.list(e,{},t):this._client.getAPIList(`/threads/${e}/messages`,n3,{query:t,...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}del(e,t,n){return this._client.delete(`/threads/${e}/messages/${t}`,{...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}}class n3 extends t0{}n4.MessagesPage=n3;class n6 extends tG{retrieve(e,t,n,r={},o){return tj(r)?this.retrieve(e,t,n,{},r):this._client.get(`/threads/${e}/runs/${t}/steps/${n}`,{query:r,...o,headers:{"OpenAI-Beta":"assistants=v2",...o?.headers}})}list(e,t,n={},r){return tj(n)?this.list(e,t,{},n):this._client.getAPIList(`/threads/${e}/runs/${t}/steps`,n8,{query:n,...r,headers:{"OpenAI-Beta":"assistants=v2",...r?.headers}})}}class n8 extends t0{}n6.RunStepsPage=n8;class n5 extends tG{constructor(){super(...arguments),this.steps=new n6(this._client)}create(e,t,n){let{include:r,...o}=t;return this._client.post(`/threads/${e}/runs`,{query:{include:r},body:o,...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers},stream:t.stream??!1})}retrieve(e,t,n){return this._client.get(`/threads/${e}/runs/${t}`,{...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}update(e,t,n,r){return this._client.post(`/threads/${e}/runs/${t}`,{body:n,...r,headers:{"OpenAI-Beta":"assistants=v2",...r?.headers}})}list(e,t={},n){return tj(t)?this.list(e,{},t):this._client.getAPIList(`/threads/${e}/runs`,n7,{query:t,...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}cancel(e,t,n){return this._client.post(`/threads/${e}/runs/${t}/cancel`,{...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}async createAndPoll(e,t,n){let r=await this.create(e,t,n);return await this.poll(e,r.id,n)}createAndStream(e,t,n){return n2.createAssistantStream(e,this._client.beta.threads.runs,t,n)}async poll(e,t,n){let r={...n?.headers,"X-Stainless-Poll-Helper":"true"};for(n?.pollIntervalMs&&(r["X-Stainless-Custom-Poll-Interval"]=n.pollIntervalMs.toString());;){let{data:o,response:i}=await this.retrieve(e,t,{...n,headers:{...n?.headers,...r}}).withResponse();switch(o.status){case"queued":case"in_progress":case"cancelling":let a=5e3;if(n?.pollIntervalMs)a=n.pollIntervalMs;else{let e=i.headers.get("openai-poll-after-ms");if(e){let t=parseInt(e);isNaN(t)||(a=t)}}await tR(a);break;case"requires_action":case"incomplete":case"cancelled":case"completed":case"failed":case"expired":return o}}}stream(e,t,n){return n2.createAssistantStream(e,this._client.beta.threads.runs,t,n)}submitToolOutputs(e,t,n,r){return this._client.post(`/threads/${e}/runs/${t}/submit_tool_outputs`,{body:n,...r,headers:{"OpenAI-Beta":"assistants=v2",...r?.headers},stream:n.stream??!1})}async submitToolOutputsAndPoll(e,t,n,r){let o=await this.submitToolOutputs(e,t,n,r);return await this.poll(e,o.id,r)}submitToolOutputsStream(e,t,n,r){return n2.createToolAssistantStream(e,t,this._client.beta.threads.runs,n,r)}}class n7 extends t0{}n5.RunsPage=n7,n5.Steps=n6,n5.RunStepsPage=n8;class n9 extends tG{constructor(){super(...arguments),this.runs=new n5(this._client),this.messages=new n4(this._client)}create(e={},t){return tj(e)?this.create({},e):this._client.post("/threads",{body:e,...t,headers:{"OpenAI-Beta":"assistants=v2",...t?.headers}})}retrieve(e,t){return this._client.get(`/threads/${e}`,{...t,headers:{"OpenAI-Beta":"assistants=v2",...t?.headers}})}update(e,t,n){return this._client.post(`/threads/${e}`,{body:t,...n,headers:{"OpenAI-Beta":"assistants=v2",...n?.headers}})}del(e,t){return this._client.delete(`/threads/${e}`,{...t,headers:{"OpenAI-Beta":"assistants=v2",...t?.headers}})}createAndRun(e,t){return this._client.post("/threads/runs",{body:e,...t,headers:{"OpenAI-Beta":"assistants=v2",...t?.headers},stream:e.stream??!1})}async createAndRunPoll(e,t){let n=await this.createAndRun(e,t);return await this.runs.poll(n.thread_id,n.id,t)}createAndRunStream(e,t){return n2.createThreadAssistantStream(e,this._client.beta.threads,t)}}n9.Runs=n5,n9.RunsPage=n7,n9.Messages=n4,n9.MessagesPage=n3;class re extends tG{constructor(){super(...arguments),this.realtime=new nJ(this._client),this.chat=new nG(this._client),this.assistants=new nS(this._client),this.threads=new n9(this._client)}}re.Realtime=nJ,re.Assistants=nS,re.AssistantsPage=nE,re.Threads=n9;class rt extends tG{create(e,t){return this._client.post("/batches",{body:e,...t})}retrieve(e,t){return this._client.get(`/batches/${e}`,t)}list(e={},t){return tj(e)?this.list({},e):this._client.getAPIList("/batches",rn,{query:e,...t})}cancel(e,t){return this._client.post(`/batches/${e}/cancel`,t)}}class rn extends t0{}rt.BatchesPage=rn;class rr extends tG{create(e,t,n){return this._client.post(`/uploads/${e}/parts`,td({body:t,...n}))}}class ro extends tG{constructor(){super(...arguments),this.parts=new rr(this._client)}create(e,t){return this._client.post("/uploads",{body:e,...t})}cancel(e,t){return this._client.post(`/uploads/${e}/cancel`,t)}complete(e,t,n){return this._client.post(`/uploads/${e}/complete`,{body:t,...n})}}function ri(e,t){let n=e.output.map(e=>{if("function_call"===e.type)return{...e,parsed_arguments:function(e,t){var n,r;let o=(n=e.tools??[],r=t.name,n.find(e=>"function"===e.type&&e.name===r));return{...t,...t,parsed_arguments:o?.$brand==="auto-parseable-tool"?o.$parseRaw(t.arguments):o?.strict?JSON.parse(t.arguments):null}}(t,e)};if("message"===e.type){let n=e.content.map(e=>{var n;return"output_text"===e.type?{...e,parsed:(n=e.text,t.text?.format?.type!=="json_schema"?null:"$parseRaw"in t.text?.format?(t.text?.format).$parseRaw(n):JSON.parse(n))}:e});return{...e,content:n}}return 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new T(`missing output at index ${e.output_index}`);"message"===n.type&&n.content.push(e.part);break}case"response.output_text.delta":{let n=t.output[e.output_index];if(!n)throw new T(`missing output at index ${e.output_index}`);if("message"===n.type){let t=n.content[e.content_index];if(!t)throw new T(`missing content at index ${e.content_index}`);if("output_text"!==t.type)throw new T(`expected content to be 'output_text', got ${t.type}`);t.text+=e.delta}break}case"response.function_call_arguments.delta":{let n=t.output[e.output_index];if(!n)throw new T(`missing output at index ${e.output_index}`);"function_call"===n.type&&(n.arguments+=e.delta);break}case"response.completed":rs(this,eG,e.response,"f")}return t},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("event",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}async finalResponse(){await this.done();let e=rc(this,eY,"f");if(!e)throw new T("stream ended without producing a ChatCompletion");return e}}class rd extends tG{constructor(){super(...arguments),this.inputItems=new rl(this._client)}create(e,t){return this._client.post("/responses",{body:e,...t,stream:e.stream??!1})._thenUnwrap(e=>("object"in e&&"response"===e.object&&ra(e),e))}retrieve(e,t={},n){return tj(t)?this.retrieve(e,{},t):this._client.get(`/responses/${e}`,{query:t,...n})}del(e,t){return this._client.delete(`/responses/${e}`,{...t,headers:{Accept:"*/*",...t?.headers}})}parse(e,t){return this._client.responses.create(e,t)._thenUnwrap(t=>ri(t,e))}stream(e,t){return ru.createResponse(this._client,e,t)}}class rf extends t0{}rd.InputItems=rl;class rp extends tG{retrieve(e,t,n,r){return this._client.get(`/evals/${e}/runs/${t}/output_items/${n}`,r)}list(e,t,n={},r){return tj(n)?this.list(e,t,{},n):this._client.getAPIList(`/evals/${e}/runs/${t}/output_items`,rh,{query:n,...r})}}class rh extends t0{}rp.OutputItemListResponsesPage=rh;class rm extends tG{constructor(){super(...arguments),this.outputItems=new rp(this._client)}create(e,t,n){return this._client.post(`/evals/${e}/runs`,{body:t,...n})}retrieve(e,t,n){return this._client.get(`/evals/${e}/runs/${t}`,n)}list(e,t={},n){return tj(t)?this.list(e,{},t):this._client.getAPIList(`/evals/${e}/runs`,rg,{query:t,...n})}del(e,t,n){return this._client.delete(`/evals/${e}/runs/${t}`,n)}cancel(e,t,n){return this._client.post(`/evals/${e}/runs/${t}`,n)}}class rg extends t0{}rm.RunListResponsesPage=rg,rm.OutputItems=rp,rm.OutputItemListResponsesPage=rh;class rv extends tG{constructor(){super(...arguments),this.runs=new rm(this._client)}create(e,t){return this._client.post("/evals",{body:e,...t})}retrieve(e,t){return this._client.get(`/evals/${e}`,t)}update(e,t,n){return this._client.post(`/evals/${e}`,{body:t,...n})}list(e={},t){return tj(e)?this.list({},e):this._client.getAPIList("/evals",ry,{query:e,...t})}del(e,t){return this._client.delete(`/evals/${e}`,t)}}class ry extends t0{}rv.EvalListResponsesPage=ry,rv.Runs=rm,rv.RunListResponsesPage=rg,n(40257);class rb extends tk{constructor({baseURL:e=tz("OPENAI_BASE_URL"),apiKey:t=tz("OPENAI_API_KEY"),organization:n=tz("OPENAI_ORG_ID")??null,project:r=tz("OPENAI_PROJECT_ID")??null,...o}={}){if(void 0===t)throw new T("The OPENAI_API_KEY environment variable is missing or empty; either provide it, or instantiate the OpenAI client with an apiKey option, like new OpenAI({ apiKey: 'My API Key' }).");let i={apiKey:t,organization:n,project:r,...o,baseURL:e||"https://api.openai.com/v1"};if(!i.dangerouslyAllowBrowser&&tW())throw new T("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew OpenAI({ apiKey, dangerouslyAllowBrowser: true });\n\nhttps://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety\n");super({baseURL:i.baseURL,timeout:i.timeout??6e5,httpAgent:i.httpAgent,maxRetries:i.maxRetries,fetch:i.fetch}),this.completions=new tY(this),this.chat=new t3(this),this.embeddings=new t6(this),this.files=new t8(this),this.images=new t7(this),this.audio=new nn(this),this.moderations=new nr(this),this.models=new no(this),this.fineTuning=new nh(this),this.vectorStores=new nx(this),this.beta=new re(this),this.batches=new rt(this),this.uploads=new ro(this),this.responses=new rd(this),this.evals=new rv(this),this._options=i,this.apiKey=t,this.organization=n,this.project=r}defaultQuery(){return 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diff --git a/litellm/proxy/_experimental/out/onboarding.html b/litellm/proxy/_experimental/out/onboarding.html
new file mode 100644
index 0000000000..0df6a53a7c
--- /dev/null
+++ b/litellm/proxy/_experimental/out/onboarding.html
@@ -0,0 +1 @@
+<!DOCTYPE html><html id="__next_error__"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="preload" as="script" fetchPriority="low" href="/litellm-asset-prefix/_next/static/chunks/webpack-a426aae3231a8df1.js"/><script src="/litellm-asset-prefix/_next/static/chunks/fd9d1056-205af899b895cbac.js" async=""></script><script src="/litellm-asset-prefix/_next/static/chunks/117-6a9f6a591033d4a0.js" async=""></script><script src="/litellm-asset-prefix/_next/static/chunks/main-app-bb35cb240fbb9dca.js" async=""></script><title>LiteLLM Dashboard</title><meta name="description" content="LiteLLM Proxy Admin UI"/><link rel="icon" href="/favicon.ico" type="image/x-icon" sizes="16x16"/><link rel="icon" href="./favicon.ico"/><meta name="next-size-adjust"/><script src="/litellm-asset-prefix/_next/static/chunks/polyfills-42372ed130431b0a.js" noModule=""></script></head><body><script 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diff --git a/litellm/proxy/_experimental/out/onboarding.txt b/litellm/proxy/_experimental/out/onboarding.txt
index 711291e1c8..5cd5ba8e84 100644
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diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index 62910ad457..c5b4ee0753 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -1,16 +1,7 @@
 import enum
 import json
 from datetime import datetime
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    Dict,
-    List,
-    Literal,
-    Optional,
-    Union,
-)
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Union
 
 import httpx
 from pydantic import (
@@ -26,11 +17,7 @@ from typing_extensions import Required, TypedDict
 from litellm._uuid import uuid
 from litellm.types.integrations.slack_alerting import AlertType
 from litellm.types.llms.openai import AllMessageValues, OpenAIFileObject
-from litellm.types.mcp import (
-    MCPAuthType,
-    MCPTransport,
-    MCPTransportType,
-)
+from litellm.types.mcp import MCPAuthType, MCPTransport, MCPTransportType
 from litellm.types.mcp_server.mcp_server_manager import MCPInfo
 from litellm.types.router import RouterErrors, UpdateRouterConfig
 from litellm.types.secret_managers.main import KeyManagementSystem
@@ -404,16 +391,16 @@ class LiteLLMRoutes(enum.Enum):
     ]
 
     key_management_routes = [
-        KeyManagementRoutes.KEY_GENERATE,
-        KeyManagementRoutes.KEY_UPDATE,
-        KeyManagementRoutes.KEY_DELETE,
-        KeyManagementRoutes.KEY_INFO,
-        KeyManagementRoutes.KEY_REGENERATE,
-        KeyManagementRoutes.KEY_GENERATE_SERVICE_ACCOUNT,
-        KeyManagementRoutes.KEY_REGENERATE_WITH_PATH_PARAM,
-        KeyManagementRoutes.KEY_LIST,
-        KeyManagementRoutes.KEY_BLOCK,
-        KeyManagementRoutes.KEY_UNBLOCK,
+        KeyManagementRoutes.KEY_GENERATE.value,
+        KeyManagementRoutes.KEY_UPDATE.value,
+        KeyManagementRoutes.KEY_DELETE.value,
+        KeyManagementRoutes.KEY_INFO.value,
+        KeyManagementRoutes.KEY_REGENERATE.value,
+        KeyManagementRoutes.KEY_GENERATE_SERVICE_ACCOUNT.value,
+        KeyManagementRoutes.KEY_REGENERATE_WITH_PATH_PARAM.value,
+        KeyManagementRoutes.KEY_LIST.value,
+        KeyManagementRoutes.KEY_BLOCK.value,
+        KeyManagementRoutes.KEY_UNBLOCK.value,
     ]
 
     management_routes = [
@@ -747,9 +734,9 @@ class GenerateRequestBase(LiteLLMPydanticObjectBase):
     allowed_cache_controls: Optional[list] = []
     config: Optional[dict] = {}
     permissions: Optional[dict] = {}
-    model_max_budget: Optional[
-        dict
-    ] = {}  # {"gpt-4": 5.0, "gpt-3.5-turbo": 5.0}, defaults to {}
+    model_max_budget: Optional[dict] = (
+        {}
+    )  # {"gpt-4": 5.0, "gpt-3.5-turbo": 5.0}, defaults to {}
 
     model_config = ConfigDict(protected_namespaces=())
     model_rpm_limit: Optional[dict] = None
@@ -788,12 +775,11 @@ class GenerateKeyRequest(KeyRequestBase):
         description="Type of key that determines default allowed routes.",
     )
     auto_rotate: Optional[bool] = Field(
-        default=False,
-        description="Whether this key should be automatically rotated"
+        default=False, description="Whether this key should be automatically rotated"
     )
     rotation_interval: Optional[str] = Field(
         default=None,
-        description="How often to rotate this key (e.g., '30d', '90d'). Required if auto_rotate=True"
+        description="How often to rotate this key (e.g., '30d', '90d'). Required if auto_rotate=True",
     )
 
 
@@ -1157,12 +1143,12 @@ class NewCustomerRequest(BudgetNewRequest):
     blocked: bool = False  # allow/disallow requests for this end-user
     budget_id: Optional[str] = None  # give either a budget_id or max_budget
     spend: Optional[float] = None
-    allowed_model_region: Optional[
-        AllowedModelRegion
-    ] = None  # require all user requests to use models in this specific region
-    default_model: Optional[
-        str
-    ] = None  # if no equivalent model in allowed region - default all requests to this model
+    allowed_model_region: Optional[AllowedModelRegion] = (
+        None  # require all user requests to use models in this specific region
+    )
+    default_model: Optional[str] = (
+        None  # if no equivalent model in allowed region - default all requests to this model
+    )
 
     @model_validator(mode="before")
     @classmethod
@@ -1184,12 +1170,12 @@ class UpdateCustomerRequest(LiteLLMPydanticObjectBase):
     blocked: bool = False  # allow/disallow requests for this end-user
     max_budget: Optional[float] = None
     budget_id: Optional[str] = None  # give either a budget_id or max_budget
-    allowed_model_region: Optional[
-        AllowedModelRegion
-    ] = None  # require all user requests to use models in this specific region
-    default_model: Optional[
-        str
-    ] = None  # if no equivalent model in allowed region - default all requests to this model
+    allowed_model_region: Optional[AllowedModelRegion] = (
+        None  # require all user requests to use models in this specific region
+    )
+    default_model: Optional[str] = (
+        None  # if no equivalent model in allowed region - default all requests to this model
+    )
 
 
 class DeleteCustomerRequest(LiteLLMPydanticObjectBase):
@@ -1263,15 +1249,15 @@ class NewTeamRequest(TeamBase):
     guardrails: Optional[List[str]] = None
     prompts: Optional[List[str]] = None
     object_permission: Optional[LiteLLM_ObjectPermissionBase] = None
-    team_member_budget: Optional[
-        float
-    ] = None  # allow user to set a budget for all team members
-    team_member_rpm_limit: Optional[
-        int
-    ] = None  # allow user to set RPM limit for all team members
-    team_member_tpm_limit: Optional[
-        int
-    ] = None  # allow user to set TPM limit for all team members
+    team_member_budget: Optional[float] = (
+        None  # allow user to set a budget for all team members
+    )
+    team_member_rpm_limit: Optional[int] = (
+        None  # allow user to set RPM limit for all team members
+    )
+    team_member_tpm_limit: Optional[int] = (
+        None  # allow user to set TPM limit for all team members
+    )
     team_member_key_duration: Optional[str] = None  # e.g. "1d", "1w", "1m"
 
     model_config = ConfigDict(protected_namespaces=())
@@ -1350,9 +1336,9 @@ class BlockKeyRequest(LiteLLMPydanticObjectBase):
 
 class AddTeamCallback(LiteLLMPydanticObjectBase):
     callback_name: str
-    callback_type: Optional[
-        Literal["success", "failure", "success_and_failure"]
-    ] = "success_and_failure"
+    callback_type: Optional[Literal["success", "failure", "success_and_failure"]] = (
+        "success_and_failure"
+    )
     callback_vars: Dict[str, str]
 
     @model_validator(mode="before")
@@ -1621,9 +1607,9 @@ class ConfigList(LiteLLMPydanticObjectBase):
     stored_in_db: Optional[bool]
     field_default_value: Any
     premium_field: bool = False
-    nested_fields: Optional[
-        List[FieldDetail]
-    ] = None  # For nested dictionary or Pydantic fields
+    nested_fields: Optional[List[FieldDetail]] = (
+        None  # For nested dictionary or Pydantic fields
+    )
 
 
 class UserHeaderMapping(LiteLLMPydanticObjectBase):
@@ -1931,7 +1917,7 @@ class UserAPIKeyAuth(
             key_alias=LITTELM_INTERNAL_HEALTH_SERVICE_ACCOUNT_NAME,
             team_alias=LITTELM_INTERNAL_HEALTH_SERVICE_ACCOUNT_NAME,
         )
-    
+
     @classmethod
     def get_litellm_cli_user_api_key_auth(cls) -> "UserAPIKeyAuth":
         """
@@ -1947,7 +1933,7 @@ class UserAPIKeyAuth(
             key_alias=LITTELM_CLI_SERVICE_ACCOUNT_NAME,
             team_alias=LITTELM_CLI_SERVICE_ACCOUNT_NAME,
         )
-    
+
     @classmethod
     def get_litellm_internal_jobs_user_api_key_auth(cls) -> "UserAPIKeyAuth":
         """
@@ -1990,9 +1976,9 @@ class LiteLLM_OrganizationMembershipTable(LiteLLMPydanticObjectBase):
     budget_id: Optional[str] = None
     created_at: datetime
     updated_at: datetime
-    user: Optional[
-        Any
-    ] = None  # You might want to replace 'Any' with a more specific type if available
+    user: Optional[Any] = (
+        None  # You might want to replace 'Any' with a more specific type if available
+    )
     litellm_budget_table: Optional[LiteLLM_BudgetTable] = None
 
     model_config = ConfigDict(protected_namespaces=())
@@ -2887,9 +2873,9 @@ class TeamModelDeleteRequest(BaseModel):
 # Organization Member Requests
 class OrganizationMemberAddRequest(OrgMemberAddRequest):
     organization_id: str
-    max_budget_in_organization: Optional[
-        float
-    ] = None  # Users max budget within the organization
+    max_budget_in_organization: Optional[float] = (
+        None  # Users max budget within the organization
+    )
 
 
 class OrganizationMemberDeleteRequest(MemberDeleteRequest):
@@ -3099,9 +3085,9 @@ class ProviderBudgetResponse(LiteLLMPydanticObjectBase):
     Maps provider names to their budget configs.
     """
 
-    providers: Dict[
-        str, ProviderBudgetResponseObject
-    ] = {}  # Dictionary mapping provider names to their budget configurations
+    providers: Dict[str, ProviderBudgetResponseObject] = (
+        {}
+    )  # Dictionary mapping provider names to their budget configurations
 
 
 class ProxyStateVariables(TypedDict):
@@ -3235,9 +3221,9 @@ class LiteLLM_JWTAuth(LiteLLMPydanticObjectBase):
     enforce_rbac: bool = False
     roles_jwt_field: Optional[str] = None  # v2 on role mappings
     role_mappings: Optional[List[RoleMapping]] = None
-    object_id_jwt_field: Optional[
-        str
-    ] = None  # can be either user / team, inferred from the role mapping
+    object_id_jwt_field: Optional[str] = (
+        None  # can be either user / team, inferred from the role mapping
+    )
     scope_mappings: Optional[List[ScopeMapping]] = None
     enforce_scope_based_access: bool = False
     enforce_team_based_model_access: bool = False
diff --git a/litellm/proxy/auth/oauth2_proxy_hook.py b/litellm/proxy/auth/oauth2_proxy_hook.py
index a900372463..7e517092b8 100644
--- a/litellm/proxy/auth/oauth2_proxy_hook.py
+++ b/litellm/proxy/auth/oauth2_proxy_hook.py
@@ -14,9 +14,9 @@ async def handle_oauth2_proxy_request(request: Request) -> UserAPIKeyAuth:
 
     verbose_proxy_logger.debug("Handling oauth2 proxy request")
     # Define the OAuth2 config mappings
-    oauth2_config_mappings: Dict[str, str] = general_settings.get(
-        "oauth2_config_mappings", {}
-    ) or {}
+    oauth2_config_mappings: Dict[str, str] = (
+        general_settings.get("oauth2_config_mappings") or {}
+    )
     verbose_proxy_logger.debug(f"Oauth2 config mappings: {oauth2_config_mappings}")
 
     if not oauth2_config_mappings:
diff --git a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py
index eec3c1a215..a51547898d 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py
@@ -22,9 +22,7 @@ from fastapi import HTTPException
 import litellm
 from litellm._logging import verbose_proxy_logger
 from litellm.caching import DualCache
-from litellm.integrations.custom_guardrail import (
-    CustomGuardrail,
-)
+from litellm.integrations.custom_guardrail import CustomGuardrail
 from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
 from litellm.llms.custom_httpx.http_handler import (
     get_async_httpx_client,
@@ -597,11 +595,11 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
         #########################################################
         ########## 2. Update the messages with the guardrail response ##########
         #########################################################
-        data[
-            "messages"
-        ] = self._update_messages_with_updated_bedrock_guardrail_response(
-            messages=new_messages,
-            bedrock_guardrail_response=bedrock_guardrail_response,
+        data["messages"] = (
+            self._update_messages_with_updated_bedrock_guardrail_response(
+                messages=new_messages,
+                bedrock_guardrail_response=bedrock_guardrail_response,
+            )
         )
 
         #########################################################
@@ -652,11 +650,11 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
         #########################################################
         ########## 2. Update the messages with the guardrail response ##########
         #########################################################
-        data[
-            "messages"
-        ] = self._update_messages_with_updated_bedrock_guardrail_response(
-            messages=new_messages,
-            bedrock_guardrail_response=bedrock_guardrail_response,
+        data["messages"] = (
+            self._update_messages_with_updated_bedrock_guardrail_response(
+                messages=new_messages,
+                bedrock_guardrail_response=bedrock_guardrail_response,
+            )
         )
 
         #########################################################
diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py
index 3e4cbec29f..af6f77b3c3 100644
--- a/litellm/proxy/hooks/parallel_request_limiter_v3.py
+++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py
@@ -303,18 +303,17 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
             if "{" in key and "}" in key:
                 start = key.find("{")
                 end = key.find("}", start)
-                hash_tag = key[start:end+1]
+                hash_tag = key[start : end + 1]
             else:
                 # Fallback for keys without hash tags
                 hash_tag = "no_hash_tag"
-            
+
             if hash_tag not in groups:
                 groups[hash_tag] = []
             groups[hash_tag].append(key)
-        
+
         return groups
 
-    
     async def _execute_redis_batch_rate_limiter_script(
         self,
         keys_to_fetch: List[str],
@@ -332,10 +331,10 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
         """
         if self.batch_rate_limiter_script is None:
             return []
-        
+
         key_groups = self._group_keys_by_hash_tag(keys_to_fetch)
         all_cache_values = []
-        
+
         for hash_tag, group_keys in key_groups.items():
             try:
                 group_cache_values = await self.batch_rate_limiter_script(
@@ -354,7 +353,7 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
                     window_size=self.window_size,
                 )
                 all_cache_values.extend(group_cache_values)
-        
+
         return all_cache_values
 
     async def should_rate_limit(
@@ -378,7 +377,9 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
         for descriptor in descriptors:
             descriptor_key = descriptor["key"]
             descriptor_value = descriptor["value"]
-            rate_limit: Optional[RateLimitDescriptorRateLimitObject] = descriptor.get("rate_limit", {}) or {}
+            rate_limit: RateLimitDescriptorRateLimitObject = (
+                descriptor.get("rate_limit") or RateLimitDescriptorRateLimitObject()
+            )
             requests_limit = rate_limit.get("requests_per_unit")
             tokens_limit = rate_limit.get("tokens_per_unit")
             max_parallel_requests_limit = rate_limit.get("max_parallel_requests")
@@ -632,26 +633,28 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
                 for i, status in enumerate(response["statuses"]):
                     if status["code"] == "OVER_LIMIT":
                         descriptor = descriptors[floor(i / 2)]
-                        
+
                         # Calculate reset time (window_start + window_size)
                         now = datetime.now().timestamp()
                         reset_time = now + self.window_size  # Conservative estimate
-                        reset_time_formatted = datetime.fromtimestamp(reset_time).strftime("%Y-%m-%d %H:%M:%S UTC")
-                        
+                        reset_time_formatted = datetime.fromtimestamp(
+                            reset_time
+                        ).strftime("%Y-%m-%d %H:%M:%S UTC")
+
                         # Handle negative remaining values more gracefully
-                        remaining_display = max(0, status['limit_remaining'])
-                        
+                        remaining_display = max(0, status["limit_remaining"])
+
                         # Create detailed error message
-                        rate_limit_type = status['rate_limit_type']
-                        current_limit = status['current_limit']
-                        
+                        rate_limit_type = status["rate_limit_type"]
+                        current_limit = status["current_limit"]
+
                         detail = (
                             f"Rate limit exceeded for {descriptor['key']}: {descriptor['value']}. "
                             f"Limit type: {rate_limit_type}. "
                             f"Current limit: {current_limit}, Remaining: {remaining_display}. "
                             f"Limit resets at: {reset_time_formatted}"
                         )
-                        
+
                         raise HTTPException(
                             status_code=429,
                             detail=detail,
@@ -693,7 +696,7 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
         )
 
         return pipeline_operations
-    
+
     async def _execute_token_increment_script(
         self,
         pipeline_operations: List["RedisPipelineIncrementOperation"],
@@ -703,15 +706,17 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
         """
         if self.token_increment_script is None:
             return
-        
+
         # Group operations by hash tag for Redis cluster compatibility
         operation_keys = [op["key"] for op in pipeline_operations]
         key_groups = self._group_keys_by_hash_tag(operation_keys)
-        
+
         for _hash_tag, group_keys in key_groups.items():
             # Get operations for this hash tag group
-            group_operations = [op for op in pipeline_operations if op["key"] in group_keys]
-            
+            group_operations = [
+                op for op in pipeline_operations if op["key"] in group_keys
+            ]
+
             keys = []
             args = []
 
@@ -731,7 +736,6 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
                 args=args,
             )
 
-
     async def async_increment_tokens_with_ttl_preservation(
         self,
         pipeline_operations: List["RedisPipelineIncrementOperation"],
@@ -757,7 +761,7 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
 
         try:
             await self._execute_token_increment_script(pipeline_operations)
-            
+
             verbose_proxy_logger.debug(
                 f"Successfully executed TTL-preserving increment for {len(pipeline_operations)} keys"
             )
@@ -811,7 +815,9 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
             )
 
             # Get metadata from kwargs
-            litellm_metadata = kwargs["litellm_params"].get(get_metadata_variable_name_from_kwargs(kwargs), {})
+            litellm_metadata = kwargs["litellm_params"].get(
+                get_metadata_variable_name_from_kwargs(kwargs), {}
+            )
             if litellm_metadata is None:
                 return
             user_api_key = litellm_metadata.get("user_api_key")
@@ -825,7 +831,9 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
             # Get total tokens from response
             total_tokens = 0
             # spot fix for /responses api
-            if (isinstance(response_obj, ModelResponse) or isinstance(response_obj, BaseLiteLLMOpenAIResponseObject)):
+            if isinstance(response_obj, ModelResponse) or isinstance(
+                response_obj, BaseLiteLLMOpenAIResponseObject
+            ):
                 _usage = getattr(response_obj, "usage", None)
                 if _usage and isinstance(_usage, Usage):
                     if rate_limit_type == "output":
@@ -943,7 +951,9 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
                 _get_parent_otel_span_from_kwargs(kwargs)
             )
             litellm_metadata = kwargs["litellm_params"]["metadata"]
-            user_api_key = litellm_metadata.get("user_api_key") if litellm_metadata else None
+            user_api_key = (
+                litellm_metadata.get("user_api_key") if litellm_metadata else None
+            )
             pipeline_operations: List[RedisPipelineIncrementOperation] = []
 
             if user_api_key:
diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py
index dc8b96656c..1227a5017b 100644
--- a/litellm/proxy/management_endpoints/ui_sso.py
+++ b/litellm/proxy/management_endpoints/ui_sso.py
@@ -10,7 +10,6 @@ Has all /sso/* routes
 
 import asyncio
 import os
-from litellm._uuid import uuid
 from copy import deepcopy
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast
 
@@ -19,6 +18,7 @@ from fastapi.responses import RedirectResponse
 
 import litellm
 from litellm._logging import verbose_proxy_logger
+from litellm._uuid import uuid
 from litellm.caching import DualCache
 from litellm.constants import MAX_SPENDLOG_ROWS_TO_QUERY
 from litellm.llms.custom_httpx.http_handler import (
@@ -115,7 +115,10 @@ def process_sso_jwt_access_token(
 
 @router.get("/sso/key/generate", tags=["experimental"], include_in_schema=False)
 async def google_login(
-    request: Request, source: Optional[str] = None, key: Optional[str] = None, existing_key: Optional[str] = None
+    request: Request,
+    source: Optional[str] = None,
+    key: Optional[str] = None,
+    existing_key: Optional[str] = None,
 ):  # noqa: PLR0915
     """
     Create Proxy API Keys using Google Workspace SSO. Requires setting PROXY_BASE_URL in .env
@@ -664,17 +667,20 @@ async def auth_callback(request: Request, state: Optional[str] = None):  # noqa:
             status_code=401,
             detail="Result not returned by SSO provider.",
         )
-    
 
     if state and state.startswith(f"{LITELLM_CLI_SESSION_TOKEN_PREFIX}:"):
         # Extract the key ID from the state
         key_id = state.split(":", 1)[1]
-        
+
         # Get existing_key from query parameters if provided
         existing_key = request.query_params.get("existing_key")
-            
-        verbose_proxy_logger.info(f"CLI SSO callback detected for key: {key_id}, existing_key: {existing_key}")
-        return await cli_sso_callback(request=request, key=key_id, existing_key=existing_key, result=result)
+
+        verbose_proxy_logger.info(
+            f"CLI SSO callback detected for key: {key_id}, existing_key: {existing_key}"
+        )
+        return await cli_sso_callback(
+            request=request, key=key_id, existing_key=existing_key, result=result
+        )
 
     return await SSOAuthenticationHandler.get_redirect_response_from_openid(
         result=result,
@@ -685,30 +691,30 @@ async def auth_callback(request: Request, state: Optional[str] = None):  # noqa:
     )
 
 
-async def _regenerate_cli_key(existing_key: str, new_key: str, user_id: Optional[str] = None) -> None:
+async def _regenerate_cli_key(
+    existing_key: str, new_key: str, user_id: Optional[str] = None
+) -> None:
     """Regenerate an existing CLI key with a new token"""
     from litellm.proxy._types import RegenerateKeyRequest, UserAPIKeyAuth
     from litellm.proxy.management_endpoints.key_management_endpoints import (
         regenerate_key_fn,
     )
-    
+
     verbose_proxy_logger.info(f"Regenerating existing CLI key: {existing_key}")
-    
+
     admin_user_dict = UserAPIKeyAuth.get_litellm_cli_user_api_key_auth()
-    
+
     regenerate_request = RegenerateKeyRequest(
         key=existing_key,
         new_key=new_key,
         duration="24hr",
         user_id=user_id,
     )
-    
+
     await regenerate_key_fn(
-        key=existing_key,
-        data=regenerate_request,
-        user_api_key_dict=admin_user_dict
+        key=existing_key, data=regenerate_request, user_api_key_dict=admin_user_dict
     )
-    
+
     verbose_proxy_logger.info(f"Regenerated CLI key: {new_key}")
 
 
@@ -720,9 +726,9 @@ async def _create_new_cli_key(
     from litellm.proxy.management_endpoints.key_management_endpoints import (
         generate_key_helper_fn,
     )
-    
+
     verbose_proxy_logger.info("Creating new CLI key")
-    
+
     await generate_key_helper_fn(
         request_type="key",
         duration="24hr",
@@ -734,13 +740,20 @@ async def _create_new_cli_key(
         table_name="key",
         token=key,
     )
-    
+
     verbose_proxy_logger.info(f"Created new CLI key: {key}")
 
 
-async def cli_sso_callback(request: Request, key: Optional[str] = None, existing_key: Optional[str] = None, result: Optional[Union[OpenID, dict]] = None):
+async def cli_sso_callback(
+    request: Request,
+    key: Optional[str] = None,
+    existing_key: Optional[str] = None,
+    result: Optional[Union[OpenID, dict]] = None,
+):
     """CLI SSO callback - regenerates existing CLI key or creates new one"""
-    verbose_proxy_logger.info(f"CLI SSO callback for key: {key}, existing_key: {existing_key}")
+    verbose_proxy_logger.info(
+        f"CLI SSO callback for key: {key}, existing_key: {existing_key}"
+    )
 
     from litellm.proxy.proxy_server import prisma_client
 
@@ -754,8 +767,10 @@ async def cli_sso_callback(request: Request, key: Optional[str] = None, existing
         raise HTTPException(
             status_code=500, detail=CommonProxyErrors.db_not_connected_error.value
         )
-    
-    parsed_openid_result = SSOAuthenticationHandler._get_user_email_and_id_from_result(result=result)
+
+    parsed_openid_result = SSOAuthenticationHandler._get_user_email_and_id_from_result(
+        result=result
+    )
     verbose_proxy_logger.debug(f"parsed_openid_result: {parsed_openid_result}")
 
     try:
@@ -783,7 +798,9 @@ async def cli_sso_callback(request: Request, key: Optional[str] = None, existing
 
     except Exception as e:
         verbose_proxy_logger.error(f"Error with CLI key: {e}")
-        raise HTTPException(status_code=500, detail=f"Failed to process CLI key: {str(e)}")
+        raise HTTPException(
+            status_code=500, detail=f"Failed to process CLI key: {str(e)}"
+        )
 
 
 @router.get("/sso/cli/poll/{key_id}", tags=["experimental"], include_in_schema=False)
@@ -874,8 +891,10 @@ async def insert_sso_user(
         auto_create_key=False,
     )
 
-    if result_openid:
-        new_user_request.metadata = {"auth_provider": result_openid.provider}
+    if result_openid and hasattr(result_openid, "provider"):
+        new_user_request.metadata = {
+            "auth_provider": getattr(result_openid, "provider")
+        }
 
     response = await new_user(
         data=new_user_request,
@@ -1052,11 +1071,13 @@ class SSOAuthenticationHandler:
                 # or a cryptographicly signed state that we can verify stateless
                 # For simplification we are using a static state, this is not perfect but some
                 # SSO providers do not allow stateless verification
-                redirect_params = SSOAuthenticationHandler._get_generic_sso_redirect_params(
-                    state=state,
-                    generic_authorization_endpoint=generic_authorization_endpoint
+                redirect_params = (
+                    SSOAuthenticationHandler._get_generic_sso_redirect_params(
+                        state=state,
+                        generic_authorization_endpoint=generic_authorization_endpoint,
+                    )
                 )
-                
+
                 return await generic_sso.get_login_redirect(**redirect_params)  # type: ignore
         raise ValueError(
             "Unknown SSO provider. Please setup SSO with client IDs https://docs.litellm.ai/docs/proxy/admin_ui_sso"
@@ -1064,26 +1085,26 @@ class SSOAuthenticationHandler:
 
     @staticmethod
     def _get_generic_sso_redirect_params(
-        state: Optional[str] = None, 
-        generic_authorization_endpoint: Optional[str] = None
+        state: Optional[str] = None,
+        generic_authorization_endpoint: Optional[str] = None,
     ) -> dict:
         """
         Get redirect parameters for Generic SSO with proper state priority handling.
-        
+
         Priority order:
         1. CLI state (if provided)
         2. GENERIC_CLIENT_STATE environment variable
         3. Generated UUID for Okta (if Okta endpoint detected)
-        
+
         Args:
             state: Optional state parameter (e.g., CLI state)
             generic_authorization_endpoint: Authorization endpoint URL
-            
+
         Returns:
             dict: Redirect parameters for SSO login
         """
         redirect_params = {}
-        
+
         if state:
             # CLI state takes priority
             # the litellm proxy cli sends the "state" parameter to the proxy server for auth. We should maintain the state parameter for the cli if it is provided
@@ -1092,8 +1113,13 @@ class SSOAuthenticationHandler:
             generic_client_state = os.getenv("GENERIC_CLIENT_STATE", None)
             if generic_client_state:
                 redirect_params["state"] = generic_client_state
-            elif generic_authorization_endpoint and "okta" in generic_authorization_endpoint:
-                redirect_params["state"] = uuid.uuid4().hex  # set state param for okta - required
+            elif (
+                generic_authorization_endpoint
+                and "okta" in generic_authorization_endpoint
+            ):
+                redirect_params["state"] = (
+                    uuid.uuid4().hex
+                )  # set state param for okta - required
 
         return redirect_params
 
@@ -1127,11 +1153,11 @@ class SSOAuthenticationHandler:
             redirect_url += sso_callback_route
         else:
             redirect_url += "/" + sso_callback_route
-            
+
         # Append existing_key as query parameter if provided
         if existing_key:
             redirect_url += f"?existing_key={existing_key}"
-            
+
         return redirect_url
 
     @staticmethod
@@ -1165,7 +1191,9 @@ class SSOAuthenticationHandler:
                 )
             return user_info
         except Exception as e:
-            verbose_proxy_logger.error(f"Error upserting SSO user into LiteLLM DB: {e}")
+            verbose_proxy_logger.exception(
+                f"Error upserting SSO user into LiteLLM DB: {e}"
+            )
             return user_info
 
     @staticmethod
@@ -1314,7 +1342,9 @@ class SSOAuthenticationHandler:
         return team_request
 
     @staticmethod
-    def _get_cli_state(source: Optional[str], key: Optional[str], existing_key: Optional[str] = None) -> Optional[str]:
+    def _get_cli_state(
+        source: Optional[str], key: Optional[str], existing_key: Optional[str] = None
+    ) -> Optional[str]:
         """
         Checks the request 'source' if a cli state token was passed in
 
@@ -1374,7 +1404,7 @@ class SSOAuthenticationHandler:
 
         if user_email is not None and (user_id is None or len(user_id) == 0):
             user_id = user_email
-        
+
         return ParsedOpenIDResult(
             user_email=user_email,
             user_id=user_id,
@@ -1408,13 +1438,16 @@ class SSOAuthenticationHandler:
         )
 
         # User is Authe'd in - generate key for the UI to access Proxy
-        parsed_openid_result = SSOAuthenticationHandler._get_user_email_and_id_from_result(result=result, generic_client_id=generic_client_id)
+        parsed_openid_result = (
+            SSOAuthenticationHandler._get_user_email_and_id_from_result(
+                result=result, generic_client_id=generic_client_id
+            )
+        )
         user_email = parsed_openid_result.get("user_email")
         user_id = parsed_openid_result.get("user_id")
         user_role = parsed_openid_result.get("user_role")
         verbose_proxy_logger.info(f"SSO callback result: {result}")
 
-
         user_info = None
         user_id_models: List = []
         max_internal_user_budget = litellm.max_internal_user_budget
diff --git a/litellm/proxy/management_helpers/team_member_permission_checks.py b/litellm/proxy/management_helpers/team_member_permission_checks.py
index 3b67472ed2..4642028b77 100644
--- a/litellm/proxy/management_helpers/team_member_permission_checks.py
+++ b/litellm/proxy/management_helpers/team_member_permission_checks.py
@@ -171,7 +171,7 @@ class TeamMemberPermissionChecks:
         """
         all_available_permissions = []
         for route in LiteLLMRoutes.key_management_routes.value:
-            all_available_permissions.append(route.value)
+            all_available_permissions.append(route)
         return all_available_permissions
 
     @staticmethod
diff --git a/litellm/proxy/management_helpers/utils.py b/litellm/proxy/management_helpers/utils.py
index e25208a216..3eab444418 100644
--- a/litellm/proxy/management_helpers/utils.py
+++ b/litellm/proxy/management_helpers/utils.py
@@ -1,6 +1,5 @@
 # What is this?
 ## Helper utils for the management endpoints (keys/users/teams)
-from litellm._uuid import uuid
 from datetime import datetime
 from functools import wraps
 from typing import Optional, Tuple
@@ -9,6 +8,7 @@ from fastapi import HTTPException, Request
 
 import litellm
 from litellm._logging import verbose_logger
+from litellm._uuid import uuid
 from litellm.proxy._types import (  # key request types; user request types; team request types; customer request types
     DeleteCustomerRequest,
     DeleteTeamRequest,
@@ -36,7 +36,7 @@ def get_new_internal_user_defaults(
     user_info = litellm.default_internal_user_params or {}
 
     returned_dict: SSOUserDefinedValues = {
-        "models": user_info.get("models", None),
+        "models": user_info.get("models") or [],
         "max_budget": user_info.get("max_budget", litellm.max_internal_user_budget),
         "budget_duration": user_info.get(
             "budget_duration", litellm.internal_user_budget_duration
diff --git a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
index a834a7a13c..5e171af525 100644
--- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
+++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
@@ -459,14 +459,6 @@ async def anthropic_proxy_route(
         region_name=None,
     )
 
-    custom_headers = {}
-    if (
-        "authorization" not in request.headers
-        and "x-api-key" not in request.headers
-        and anthropic_api_key is not None
-    ):
-        custom_headers["x-api-key"] = "{}".format(anthropic_api_key)
-
     ## check for streaming
     is_streaming_request = await is_streaming_request_fn(request)
 
@@ -474,7 +466,7 @@ async def anthropic_proxy_route(
     endpoint_func = create_pass_through_route(
         endpoint=endpoint,
         target=str(updated_url),
-        custom_headers=custom_headers,
+        custom_headers={"x-api-key": "{}".format(anthropic_api_key)},
         _forward_headers=True,
     )  # dynamically construct pass-through endpoint based on incoming path
     received_value = await endpoint_func(
diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
index 628784f6f1..c0042133b4 100644
--- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
+++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
@@ -3,7 +3,6 @@ import asyncio
 import copy
 import json
 import traceback
-from litellm._uuid import uuid
 from base64 import b64encode
 from datetime import datetime
 from typing import Dict, List, Optional, Tuple, Union
@@ -25,6 +24,7 @@ from starlette.datastructures import UploadFile as StarletteUploadFile
 
 import litellm
 from litellm._logging import verbose_proxy_logger
+from litellm._uuid import uuid
 from litellm.constants import MAXIMUM_TRACEBACK_LINES_TO_LOG
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
@@ -424,10 +424,10 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils):
 
         for field_name, field_value in form_data.items():
             if isinstance(field_value, (StarletteUploadFile, UploadFile)):
-                files[
-                    field_name
-                ] = await HttpPassThroughEndpointHelpers._build_request_files_from_upload_file(
-                    upload_file=field_value
+                files[field_name] = (
+                    await HttpPassThroughEndpointHelpers._build_request_files_from_upload_file(
+                        upload_file=field_value
+                    )
                 )
             else:
                 form_data_dict[field_name] = field_value
@@ -476,7 +476,11 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils):
                 user_api_key_request_route=user_api_key_dict.request_route,
                 user_api_key_spend=user_api_key_dict.spend,
                 user_api_key_max_budget=user_api_key_dict.max_budget,
-                user_api_key_budget_reset_at=user_api_key_dict.budget_reset_at.isoformat() if user_api_key_dict.budget_reset_at else None,
+                user_api_key_budget_reset_at=(
+                    user_api_key_dict.budget_reset_at.isoformat()
+                    if user_api_key_dict.budget_reset_at
+                    else None
+                ),
             )
         )
 
@@ -496,7 +500,7 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils):
 
         kwargs = {
             "litellm_params": {
-                **litellm_params_in_body,
+                **litellm_params_in_body,  # type: ignore
                 "metadata": _metadata,
                 "proxy_server_request": {
                     "url": str(request.url),
@@ -509,9 +513,9 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils):
             "passthrough_logging_payload": passthrough_logging_payload,
         }
 
-        logging_obj.model_call_details[
-            "passthrough_logging_payload"
-        ] = passthrough_logging_payload
+        logging_obj.model_call_details["passthrough_logging_payload"] = (
+            passthrough_logging_payload
+        )
 
         return kwargs
 
@@ -923,7 +927,6 @@ def create_pass_through_route(
 ):
     # check if target is an adapter.py or a url
     from litellm._uuid import uuid
-
     from litellm.proxy.types_utils.utils import get_instance_fn
 
     try:
@@ -1367,7 +1370,6 @@ async def create_pass_through_endpoints(
     Create new pass-through endpoint
     """
     from litellm._uuid import uuid
-
     from litellm.proxy.proxy_server import (
         get_config_general_settings,
         update_config_general_settings,
diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py
index 6d2e56c444..f031b9ffbd 100644
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@@ -637,11 +637,6 @@ async def proxy_startup_event(app: FastAPI):
         user_api_key_cache=user_api_key_cache,
     )
 
-    if use_background_health_checks:
-        asyncio.create_task(
-            _run_background_health_check()
-        )  # start the background health check coroutine.
-
     if prompt_injection_detection_obj is not None:  # [TODO] - REFACTOR THIS
         prompt_injection_detection_obj.update_environment(router=llm_router)
 
@@ -664,6 +659,12 @@ async def proxy_startup_event(app: FastAPI):
 
         await ProxyStartupEvent._update_default_team_member_budget()
 
+    # Start background health checks AFTER models are loaded and index is built
+    if use_background_health_checks:
+        asyncio.create_task(
+            _run_background_health_check()
+        )  # start the background health check coroutine.
+
     ## [Optional] Initialize dd tracer
     ProxyStartupEvent._init_dd_tracer()
 
diff --git a/litellm/proxy/spend_tracking/spend_tracking_utils.py b/litellm/proxy/spend_tracking/spend_tracking_utils.py
index 7b78200b04..a0c2c929cb 100644
--- a/litellm/proxy/spend_tracking/spend_tracking_utils.py
+++ b/litellm/proxy/spend_tracking/spend_tracking_utils.py
@@ -10,7 +10,7 @@ from pydantic import BaseModel
 
 import litellm
 from litellm._logging import verbose_proxy_logger
-from litellm.constants import REDACTED_BY_LITELM_STRING, MAX_STRING_LENGTH_PROMPT_IN_DB
+from litellm.constants import MAX_STRING_LENGTH_PROMPT_IN_DB, REDACTED_BY_LITELM_STRING
 from litellm.litellm_core_utils.core_helpers import get_litellm_metadata_from_kwargs
 from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
 from litellm.proxy._types import SpendLogsMetadata, SpendLogsPayload
@@ -21,6 +21,7 @@ from litellm.types.utils import (
     StandardLoggingModelInformation,
     StandardLoggingPayload,
     StandardLoggingVectorStoreRequest,
+    VectorStoreSearchResponse,
 )
 from litellm.utils import get_end_user_id_for_cost_tracking
 
@@ -297,7 +298,9 @@ def get_logging_payload(  # noqa: PLR0915
         id = f"{id}_cache_hit{time.time()}"  # SpendLogs does not allow duplicate request_id
 
     mcp_namespaced_tool_name = None
-    mcp_tool_call_metadata = clean_metadata.get("mcp_tool_call_metadata", {})
+    mcp_tool_call_metadata: Optional[StandardLoggingMCPToolCall] = clean_metadata.get(
+        "mcp_tool_call_metadata"
+    )
     if mcp_tool_call_metadata is not None:
         mcp_namespaced_tool_name = mcp_tool_call_metadata.get(
             "namespaced_tool_name", None
@@ -505,23 +508,23 @@ def _sanitize_request_body_for_spend_logs_payload(
                 # This split ensures we keep more context from the end of conversations
                 start_ratio = 0.35
                 end_ratio = 0.65
-                
+
                 # Calculate character distribution
                 start_chars = int(MAX_STRING_LENGTH_PROMPT_IN_DB * start_ratio)
                 end_chars = int(MAX_STRING_LENGTH_PROMPT_IN_DB * end_ratio)
-                
+
                 # Ensure we don't exceed the total limit
                 total_keep = start_chars + end_chars
                 if total_keep > MAX_STRING_LENGTH_PROMPT_IN_DB:
                     end_chars = MAX_STRING_LENGTH_PROMPT_IN_DB - start_chars
-                
+
                 # If the string length is less than what we want to keep, just truncate normally
                 if len(value) <= MAX_STRING_LENGTH_PROMPT_IN_DB:
                     return value
-                
+
                 # Calculate how many characters are being skipped
                 skipped_chars = len(value) - total_keep
-                
+
                 # Build the truncated string: beginning + truncation marker + end
                 truncated_value = (
                     f"{value[:start_chars]}"
@@ -567,8 +570,9 @@ def _get_vector_store_request_for_spend_logs_payload(
     if vector_store_request_metadata is None:
         return None
     for vector_store_request in vector_store_request_metadata:
-        vector_store_search_response = (
-            vector_store_request.get("vector_store_search_response", {}) or {}
+        vector_store_search_response: VectorStoreSearchResponse = (
+            vector_store_request.get("vector_store_search_response")
+            or VectorStoreSearchResponse()
         )
         response_data = vector_store_search_response.get("data", []) or []
         for response_item in response_data:
diff --git a/litellm/router.py b/litellm/router.py
index f093096f84..3cf99a4b21 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -17,7 +17,6 @@ import logging
 import threading
 import time
 import traceback
-from litellm._uuid import uuid
 from collections import defaultdict
 from functools import lru_cache
 from typing import (
@@ -45,6 +44,7 @@ import litellm.litellm_core_utils
 import litellm.litellm_core_utils.exception_mapping_utils
 from litellm import get_secret_str
 from litellm._logging import verbose_router_logger
+from litellm._uuid import uuid
 from litellm.caching.caching import (
     DualCache,
     InMemoryCache,
@@ -409,7 +409,12 @@ class Router:
         )  # {"TEAM_ID": PatternMatchRouter}
         self.auto_routers: Dict[str, "AutoRouter"] = {}
 
+        # Initialize model ID to deployment index mapping for O(1) lookups
+        self.model_id_to_deployment_index_map: Dict[str, int] = {}
+        
         if model_list is not None:
+            # Build model index immediately to enable O(1) lookups from the start
+            self._build_model_id_to_deployment_index_map(model_list)
             model_list = copy.deepcopy(model_list)
             self.set_model_list(model_list)
             self.healthy_deployments: List = self.model_list  # type: ignore
@@ -2005,11 +2010,17 @@ class Router:
 
         # Filter out prompt management specific parameters from data before merging
         prompt_management_params = {
-            "bitbucket_config", "dotprompt_config", "prompt_id", 
-            "prompt_variables", "prompt_label", "prompt_version"
+            "bitbucket_config",
+            "dotprompt_config",
+            "prompt_id",
+            "prompt_variables",
+            "prompt_label",
+            "prompt_version",
         }
-        filtered_data = {k: v for k, v in data.items() if k not in prompt_management_params}
-        
+        filtered_data = {
+            k: v for k, v in data.items() if k not in prompt_management_params
+        }
+
         kwargs = {**filtered_data, **kwargs, **optional_params}
         kwargs["model"] = model
         kwargs["messages"] = messages
@@ -3436,7 +3447,7 @@ class Router:
             *[try_retrieve_batch(model) for model in filtered_model_list]
         )
 
-        final_results = {
+        final_results: Dict = {
             "object": "list",
             "data": [],
             "first_id": None,
@@ -4108,7 +4119,9 @@ class Router:
         """
         model_group = kwargs.get("model")
         response = original_function(*args, **kwargs)
-        if coroutine_checker.is_async_callable(response) or inspect.isawaitable(response):
+        if coroutine_checker.is_async_callable(response) or inspect.isawaitable(
+            response
+        ):
             response = await response
         ## PROCESS RESPONSE HEADERS
         response = await self.set_response_headers(
@@ -4517,7 +4530,9 @@ class Router:
                 _time_to_cooldown = self.cooldown_time
 
             if isinstance(_model_info, dict):
-                deployment_id = _model_info.get("id", None)
+                deployment_id: Optional[str] = _model_info.get("id")
+                if deployment_id is None:
+                    return False
                 increment_deployment_failures_for_current_minute(
                     litellm_router_instance=self,
                     deployment_id=deployment_id,
@@ -4974,7 +4989,7 @@ class Router:
 
             model = deployment.to_json(exclude_none=True)
 
-            self.model_list.append(model)
+            self._add_model_to_list_and_index_map(model=model, model_id=deployment.model_info.id)
             return deployment
         except Exception as e:
             if self.ignore_invalid_deployments:
@@ -5085,6 +5100,7 @@ class Router:
     def set_model_list(self, model_list: list):
         original_model_list = copy.deepcopy(model_list)
         self.model_list = []
+        self.model_id_to_deployment_index_map = {}  # Reset the index
         # we add api_base/api_key each model so load balancing between azure/gpt on api_base1 and api_base2 works
 
         for model in original_model_list:
@@ -5134,12 +5150,12 @@ class Router:
         # Check if this is a prompt management model before validating as LLM provider
         litellm_model = deployment.litellm_params.model
         is_prompt_management_model = False
-        
+
         if "/" in litellm_model:
             split_litellm_model = litellm_model.split("/")[0]
             if split_litellm_model in litellm._known_custom_logger_compatible_callbacks:
                 is_prompt_management_model = True
-        
+
         if is_prompt_management_model:
             # For prompt management models, skip LLM provider validation
             # The actual model will be resolved at runtime from the prompt file
@@ -5229,11 +5245,12 @@ class Router:
         #     litellm_router_instance=self, model=deployment.to_json(exclude_none=True)
         # )
 
-        self._initialize_deployment_for_pass_through(
-            deployment=deployment,
-            custom_llm_provider=custom_llm_provider,
-            model=deployment.litellm_params.model,
-        )
+        if custom_llm_provider is not None:
+            self._initialize_deployment_for_pass_through(
+                deployment=deployment,
+                custom_llm_provider=custom_llm_provider,
+                model=deployment.litellm_params.model,
+            )
 
         #########################################################
         # Check if this is an auto-router deployment
@@ -5323,10 +5340,42 @@ class Router:
         self._add_deployment(deployment=deployment)
 
         # add to model names
-        self.model_list.append(_deployment)
+        self._add_model_to_list_and_index_map(model=_deployment, model_id=deployment.model_info.id)
         self.model_names.append(deployment.model_name)
         return deployment
 
+    def _update_deployment_indices_after_removal(self, model_id: str, removal_idx: int) -> None:
+        """
+        Helper method to update deployment indices after a deployment has been removed from model_list.
+        
+        Parameters:
+        - model_id: str - the id of the deployment that was removed
+        - removal_idx: int - the index where the deployment was removed from model_list
+        """
+        # Update indices for all models after the removed one
+        for deployment_id, idx in self.model_id_to_deployment_index_map.items():
+            if idx > removal_idx:
+                self.model_id_to_deployment_index_map[deployment_id] = idx - 1
+        # Remove the deleted model from index
+        if model_id in self.model_id_to_deployment_index_map:
+            del self.model_id_to_deployment_index_map[model_id]
+
+
+    def _add_model_to_list_and_index_map(self, model: dict, model_id: Optional[str] = None) -> None:
+        """
+        Helper method to add a model to the model_list and update the model_id_to_deployment_index_map.
+        
+        Parameters:
+        - model: dict - the model to add to the list
+        - model_id: Optional[str] - the model ID to use for indexing. If None, will try to get from model["model_info"]["id"]
+        """
+        self.model_list.append(model)
+        # Update model index for O(1) lookup
+        if model_id is not None:
+            self.model_id_to_deployment_index_map[model_id] = len(self.model_list) - 1
+        elif model.get("model_info", {}).get("id") is not None:
+            self.model_id_to_deployment_index_map[model["model_info"]["id"]] = len(self.model_list) - 1
+
     def upsert_deployment(self, deployment: Deployment) -> Optional[Deployment]:
         """
         Add or update deployment
@@ -5352,12 +5401,15 @@ class Router:
                 # if there is a new litellm param -> then update the deployment
                 # remove the previous deployment
                 removal_idx: Optional[int] = None
-                for idx, model in enumerate(self.model_list):
-                    if model["model_info"]["id"] == deployment.model_info.id:
-                        removal_idx = idx
+                deployment_id = deployment.model_info.id
+                deployment_fast_mapping = self.model_id_to_deployment_index_map
+                
+                if deployment_id in deployment_fast_mapping:
+                    removal_idx = deployment_fast_mapping[deployment_id]
 
-                if removal_idx is not None:
-                    self.model_list.pop(removal_idx)
+                    if removal_idx is not None:
+                        self.model_list.pop(removal_idx)
+                        self._update_deployment_indices_after_removal(model_id=deployment_id, removal_idx=removal_idx)
 
             # if the model_id is not in router
             self.add_deployment(deployment=deployment)
@@ -5381,13 +5433,14 @@ class Router:
         - OR None (if deleted deployment not found)
         """
         deployment_idx = None
-        for idx, m in enumerate(self.model_list):
-            if m["model_info"]["id"] == id:
-                deployment_idx = idx
+        if id in self.model_id_to_deployment_index_map:
+            deployment_idx = self.model_id_to_deployment_index_map[id]
 
         try:
             if deployment_idx is not None:
+                # Pop the item from the list first
                 item = self.model_list.pop(deployment_idx)
+                self._update_deployment_indices_after_removal(model_id=id, removal_idx=deployment_idx)
                 return item
             else:
                 return None
@@ -5400,15 +5453,17 @@ class Router:
 
         Raise Exception -> if model found in invalid format
         """
-        for model in self.model_list:
-            if "model_info" in model and "id" in model["model_info"]:
-                if model_id == model["model_info"]["id"]:
-                    if isinstance(model, dict):
-                        return Deployment(**model)
-                    elif isinstance(model, Deployment):
-                        return model
-                    else:
-                        raise Exception("Model invalid format - {}".format(type(model)))
+        # Use O(1) lookup via model_id_to_deployment_index_map only
+        if model_id in self.model_id_to_deployment_index_map:
+            idx = self.model_id_to_deployment_index_map[model_id]
+            model = self.model_list[idx]
+            if isinstance(model, dict):
+                return Deployment(**model)
+            elif isinstance(model, Deployment):
+                return model
+            else:
+                raise Exception("Model invalid format - {}".format(type(model)))
+        
         return None
 
     def get_deployment_credentials(self, model_id: str) -> Optional[dict]:
@@ -6026,6 +6081,31 @@ class Router:
                         additional_headers[header] = value
         return response
 
+    def _build_model_id_to_deployment_index_map(self, model_list: list):
+        """
+        Build model index from model list to enable O(1) lookups immediately.
+        This is called during initialization to avoid the race condition where
+        requests arrive before model_id_to_deployment_index_map is populated.
+        """
+        # First populate the model_list
+        self.model_list = []
+        for _, model in enumerate(model_list):
+            # Extract model_info from the model dict
+            model_info = model.get("model_info", {})
+            model_id = model_info.get("id")
+            
+            # If no ID exists, generate one using the same logic as set_model_list
+            if model_id is None:
+                model_name = model.get("model_name", "")
+                litellm_params = model.get("litellm_params", {})
+                model_id = self._generate_model_id(model_name, litellm_params)
+                # Update the model_info in the original list
+                if "model_info" not in model:
+                    model["model_info"] = {}
+                model["model_info"]["id"] = model_id
+            
+            self._add_model_to_list_and_index_map(model=model, model_id=model_id)
+
     def get_model_ids(
         self, model_name: Optional[str] = None, exclude_team_models: bool = False
     ) -> List[str]:
diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py
index 4adb751d90..434035b809 100644
--- a/litellm/types/llms/openai.py
+++ b/litellm/types/llms/openai.py
@@ -43,9 +43,12 @@ from openai.types.responses.response import (
 
 # Handle OpenAI SDK version compatibility for Text type
 try:
-    from openai.types.responses.response_create_params import (
-        Text as ResponseText,  # type: ignore
+    # fmt: off
+    from openai.types.responses.response_create_params import ( # type: ignore[attr-defined]
+        Text as ResponseText,  # type: ignore[attr-defined]
     )
+
+    # fmt: on
 except (ImportError, AttributeError):
     # Fall back to the concrete config type available in all SDK versions
     from openai.types.responses.response_text_config_param import (
@@ -1308,7 +1311,7 @@ class MCPListToolsFailedEvent(BaseLiteLLMOpenAIResponseObject):
     item_id: str
 
 
-# MCP Call Events  
+# MCP Call Events
 class MCPCallInProgressEvent(BaseLiteLLMOpenAIResponseObject):
     type: Literal[ResponsesAPIStreamEvents.MCP_CALL_IN_PROGRESS]
     sequence_number: int
diff --git a/pyproject.toml b/pyproject.toml
index 579f4f61db..61afc2b1de 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.77.4"
+version = "1.77.5"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -157,7 +157,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.77.4"
+version = "1.77.5"
 version_files = [
     "pyproject.toml:^version"
 ]
diff --git a/test_image_edit.png b/test_image_edit.png
new file mode 100644
index 0000000000..0380114f24
Binary files /dev/null and b/test_image_edit.png differ
diff --git a/tests/enterprise/litellm_enterprise/integrations/test_prometheus_unit_tests.py b/tests/enterprise/litellm_enterprise/integrations/test_prometheus_unit_tests.py
index b06bb5f475..b0bd88b4e3 100644
--- a/tests/enterprise/litellm_enterprise/integrations/test_prometheus_unit_tests.py
+++ b/tests/enterprise/litellm_enterprise/integrations/test_prometheus_unit_tests.py
@@ -166,7 +166,7 @@ async def test_prometheus_metric_tracking():
             {
                 "model_name": "gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
diff --git a/tests/image_gen_tests/test_image_generation.py b/tests/image_gen_tests/test_image_generation.py
index c390034295..478a2f04c9 100644
--- a/tests/image_gen_tests/test_image_generation.py
+++ b/tests/image_gen_tests/test_image_generation.py
@@ -118,19 +118,6 @@ class TestVertexImageGeneration(BaseImageGenTest):
             "n": 1,
         }
 
-
-class TestBedrockSd3(BaseImageGenTest):
-    def get_base_image_generation_call_args(self) -> dict:
-        litellm.in_memory_llm_clients_cache = InMemoryCache()
-        return {"model": "bedrock/stability.sd3-large-v1:0"}
-
-
-class TestBedrockSd1(BaseImageGenTest):
-    def get_base_image_generation_call_args(self) -> dict:
-        litellm.in_memory_llm_clients_cache = InMemoryCache()
-        return {"model": "bedrock/stability.sd3-large-v1:0"}
-
-
 class TestBedrockNovaCanvasTextToImage(BaseImageGenTest):
     def get_base_image_generation_call_args(self) -> dict:
         litellm.in_memory_llm_clients_cache = InMemoryCache()
diff --git a/tests/llm_translation/test_azure_ai.py b/tests/llm_translation/test_azure_ai.py
index 943020c1bb..241238cd22 100644
--- a/tests/llm_translation/test_azure_ai.py
+++ b/tests/llm_translation/test_azure_ai.py
@@ -313,3 +313,29 @@ async def test_azure_gpt5_reasoning(model):
     )
     print("response: ", response)
     assert response.choices[0].message.content is not None
+
+
+
+def test_completion_azure():
+    try:
+        litellm.set_verbose = False
+        ## Test azure call
+        response = completion(
+            model="azure/gpt-4.1-nano",
+            messages=[
+                {
+                    "role": "user",
+                    "content": "Hello, how are you?",
+                }
+            ],
+            api_key="os.environ/AZURE_API_KEY",
+        )
+        print(f"response: {response}")
+        print(f"response hidden params: {response._hidden_params}")
+        print(response)
+
+        cost = completion_cost(completion_response=response)
+        assert cost > 0.0
+        print("Cost for azure completion request", cost)
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
diff --git a/tests/llm_translation/test_azure_openai.py b/tests/llm_translation/test_azure_openai.py
index 131e23b1c5..b9f20ffd25 100644
--- a/tests/llm_translation/test_azure_openai.py
+++ b/tests/llm_translation/test_azure_openai.py
@@ -137,7 +137,7 @@ def test_azure_extra_headers(input, call_type, header_value):
                 func = image_generation
 
             data = {
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com",
                 "api_version": "2023-07-01-preview",
                 "api_key": "my-azure-api-key",
diff --git a/tests/local_testing/example_config_yaml/azure_config.yaml b/tests/local_testing/example_config_yaml/azure_config.yaml
index 111813c884..a0300bd6d1 100644
--- a/tests/local_testing/example_config_yaml/azure_config.yaml
+++ b/tests/local_testing/example_config_yaml/azure_config.yaml
@@ -1,7 +1,7 @@
 model_list:
   - model_name: gpt-4-team1
     litellm_params:
-      model: azure/chatgpt-v-3
+      model: azure/gpt-4.1-nano
       api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
       api_version: "2023-05-15"
       api_key:  os.environ/AZURE_API_KEY
diff --git a/tests/local_testing/test_acooldowns_router.py b/tests/local_testing/test_acooldowns_router.py
index 0c385133e3..79345b60b1 100644
--- a/tests/local_testing/test_acooldowns_router.py
+++ b/tests/local_testing/test_acooldowns_router.py
@@ -26,7 +26,7 @@ model_list = [
     {  # list of model deployments
         "model_name": "gpt-3.5-turbo",  # openai model name
         "litellm_params": {  # params for litellm completion/embedding call
-            "model": "azure/chatgpt-v-3",
+            "model": "azure/gpt-4.1-nano",
             "api_key": "bad-key",
             "api_version": os.getenv("AZURE_API_VERSION"),
             "api_base": os.getenv("AZURE_API_BASE"),
@@ -180,7 +180,7 @@ async def test_cooldown_same_model_name(sync_mode):
                 model_ids.append(model["model_info"]["id"])
             print("\n litellm model ids ", model_ids)
 
-            # example litellm_model_names ['azure/chatgpt-v-3-ModelID-64321', 'azure/chatgpt-v-3-ModelID-63960']
+            # example litellm_model_names ['azure/gpt-4.1-nano-ModelID-64321', 'azure/gpt-4.1-nano-ModelID-63960']
             assert (
                 model_ids[0] != model_ids[1]
             )  # ensure both models have a uuid added, and they have different names
@@ -197,7 +197,7 @@ async def test_cooldown_same_model_name(sync_mode):
                 model_ids.append(model["model_info"]["id"])
             print("\n litellm model ids ", model_ids)
 
-            # example litellm_model_names ['azure/chatgpt-v-3-ModelID-64321', 'azure/chatgpt-v-3-ModelID-63960']
+            # example litellm_model_names ['azure/gpt-4.1-nano-ModelID-64321', 'azure/gpt-4.1-nano-ModelID-63960']
             assert (
                 model_ids[0] != model_ids[1]
             )  # ensure both models have a uuid added, and they have different names
diff --git a/tests/local_testing/test_alangfuse.py b/tests/local_testing/test_alangfuse.py
index 75dc07f9d5..c196e4a3b3 100644
--- a/tests/local_testing/test_alangfuse.py
+++ b/tests/local_testing/test_alangfuse.py
@@ -194,7 +194,7 @@ def create_async_task(**completion_kwargs):
     By default a standard set of arguments are used for the litellm.acompletion function.
     """
     completion_args = {
-        "model": "azure/chatgpt-v-3",
+        "model": "azure/gpt-4.1-nano",
         "api_version": "2024-02-01",
         "messages": [{"role": "user", "content": "This is a test"}],
         "max_tokens": 5,
diff --git a/tests/local_testing/test_amazing_vertex_completion.py b/tests/local_testing/test_amazing_vertex_completion.py
index ebaafcf43b..d7b1f95d5f 100644
--- a/tests/local_testing/test_amazing_vertex_completion.py
+++ b/tests/local_testing/test_amazing_vertex_completion.py
@@ -764,7 +764,7 @@ def test_gemini_pro_grounding(value_in_dict):
 
 
 # @pytest.mark.skip(reason="exhausted vertex quota. need to refactor to mock the call")
-@pytest.mark.parametrize("model", ["vertex_ai_beta/gemini-1.5-pro"])  # "vertex_ai",
+@pytest.mark.parametrize("model", ["vertex_ai_beta/gemini-2.5-flash-lite"])  # "vertex_ai",
 @pytest.mark.parametrize("sync_mode", [True])  # "vertex_ai",
 @pytest.mark.asyncio
 @pytest.mark.flaky(retries=3, delay=1)
diff --git a/tests/local_testing/test_azure_openai.py b/tests/local_testing/test_azure_openai.py
index ceafc37488..0c6147b584 100644
--- a/tests/local_testing/test_azure_openai.py
+++ b/tests/local_testing/test_azure_openai.py
@@ -47,7 +47,7 @@ async def test_aaaaazure_tenant_id_auth(respx_mock: MockRouter):
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_base": os.getenv("AZURE_API_BASE"),
                     "tenant_id": os.getenv("AZURE_TENANT_ID"),
                     "client_id": os.getenv("AZURE_CLIENT_ID"),
@@ -96,6 +96,6 @@ async def test_aaaaazure_tenant_id_auth(respx_mock: MockRouter):
 
         assert json_body == {
             "messages": [{"role": "user", "content": "Hello world!"}],
-            "model": "chatgpt-v-3",
+            "model": "gpt-4.1-nano",
             "stream": False,
         }
diff --git a/tests/local_testing/test_azure_perf.py b/tests/local_testing/test_azure_perf.py
index bc6d694b78..0ec3718983 100644
--- a/tests/local_testing/test_azure_perf.py
+++ b/tests/local_testing/test_azure_perf.py
@@ -18,7 +18,7 @@
 #     {
 #         "model_name": "azure-test",
 #         "litellm_params": {
-#             "model": "azure/chatgpt-v-3",
+#             "model": "azure/gpt-4.1-nano",
 #             "api_key": os.getenv("AZURE_API_KEY"),
 #             "api_base": os.getenv("AZURE_API_BASE"),
 #             "api_version": os.getenv("AZURE_API_VERSION"),
diff --git a/tests/local_testing/test_caching.py b/tests/local_testing/test_caching.py
index 3fb3ae3db4..ee3082a9d3 100644
--- a/tests/local_testing/test_caching.py
+++ b/tests/local_testing/test_caching.py
@@ -1627,7 +1627,7 @@ def test_get_cache_key():
 
         embedding_cache_key = cache_instance.get_cache_key(
             **{
-                "model": "azure/azure-embedding-model",
+                "model": "azure/text-embedding-ada-002",
                 "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com/",
                 "api_key": "",
                 "api_version": "2023-07-01-preview",
@@ -1642,19 +1642,19 @@ def test_get_cache_key():
         print(embedding_cache_key)
 
         embedding_cache_key_str = (
-            "model: azure/azure-embedding-modelinput: ['hi who is ishaan']"
+            "model: azure/text-embedding-ada-002input: ['hi who is ishaan']"
         )
         hash_object = hashlib.sha256(embedding_cache_key_str.encode())
         # Hexadecimal representation of the hash
         hash_hex = hash_object.hexdigest()
         assert (
             embedding_cache_key == hash_hex
-        ), f"{embedding_cache_key} != 'model: azure/azure-embedding-modelinput: ['hi who is ishaan']'. The same kwargs should have the same cache key across runs"
+        ), f"{embedding_cache_key} != 'model: azure/text-embedding-ada-002input: ['hi who is ishaan']'. The same kwargs should have the same cache key across runs"
 
         # Proxy - embedding cache, test if embedding key, gets model_group and not model
         embedding_cache_key_2 = cache_instance.get_cache_key(
             **{
-                "model": "azure/azure-embedding-model",
+                "model": "azure/text-embedding-ada-002",
                 "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com/",
                 "api_key": "",
                 "api_version": "2023-07-01-preview",
@@ -1689,7 +1689,7 @@ def test_get_cache_key():
                         "content-length": "80",
                     },
                     "model_group": "EMBEDDING_MODEL_GROUP",
-                    "deployment": "azure/azure-embedding-model-ModelID-azure/azure-embedding-modelhttps://openai-gpt-4-test-v-1.openai.azure.com/2023-07-01-preview",
+                    "deployment": "azure/text-embedding-ada-002-ModelID-azure/text-embedding-ada-002https://openai-gpt-4-test-v-1.openai.azure.com/2023-07-01-preview",
                 },
                 "model_info": {
                     "mode": "embedding",
diff --git a/tests/local_testing/test_caching_ssl.py b/tests/local_testing/test_caching_ssl.py
index 7357f89c42..574b8ccaea 100644
--- a/tests/local_testing/test_caching_ssl.py
+++ b/tests/local_testing/test_caching_ssl.py
@@ -58,7 +58,7 @@ def test_caching_router():
             {
                 "model_name": "gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
diff --git a/tests/local_testing/test_class.py b/tests/local_testing/test_class.py
index e6b711efe8..b83a915770 100644
--- a/tests/local_testing/test_class.py
+++ b/tests/local_testing/test_class.py
@@ -55,7 +55,7 @@
 # #             {
 # #                 "model_name": "gpt-3.5-turbo",  # openai model name
 # #                 "litellm_params": {  # params for litellm completion/embedding call
-# #                     "model": "azure/chatgpt-v-3",
+# #                     "model": "azure/gpt-4.1-nano",
 # #                     "api_key": os.getenv("AZURE_API_KEY"),
 # #                     "api_version": os.getenv("AZURE_API_VERSION"),
 # #                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -93,7 +93,7 @@
 # #             {
 # #                 "model_name": "gpt-3.5-turbo",  # openai model name
 # #                 "litellm_params": {  # params for litellm completion/embedding call
-# #                     "model": "azure/chatgpt-v-3",
+# #                     "model": "azure/gpt-4.1-nano",
 # #                     "api_key": os.getenv("AZURE_API_KEY"),
 # #                     "api_version": os.getenv("AZURE_API_VERSION"),
 # #                     "api_base": os.getenv("AZURE_API_BASE"),
diff --git a/tests/local_testing/test_completion.py b/tests/local_testing/test_completion.py
index 9fc370fdcc..6429bb6b49 100644
--- a/tests/local_testing/test_completion.py
+++ b/tests/local_testing/test_completion.py
@@ -712,7 +712,7 @@ def encode_image(image_path):
     "model",
     [
         "gpt-4o",
-        "azure/gpt-4o-new-test",
+        "azure/gpt-4.1-nano",
         "anthropic/claude-3-opus-20240229",
     ],
 )  #
@@ -1746,7 +1746,7 @@ def test_completion_openai():
     "model, api_version",
     [
         # ("gpt-4o-2024-08-06", None),
-        # ("azure/chatgpt-v-3", None),
+        # ("azure/gpt-4.1-nano", None),
         ("bedrock/anthropic.claude-3-sonnet-20240229-v1:0", None),
         # ("azure/gpt-4o-new-test", "2024-08-01-preview"),
     ],
@@ -2417,7 +2417,7 @@ def test_completion_azure_extra_headers():
         litellm.client_session = http_client
         try:
             response = completion(
-                model="azure/chatgpt-v-3",
+                model="azure/gpt-4.1-nano",
                 messages=messages,
                 api_base=os.getenv("AZURE_API_BASE"),
                 api_version="2023-07-01-preview",
@@ -2466,7 +2466,7 @@ def test_completion_azure_ad_token():
         litellm.client_session = http_client
         try:
             response = completion(
-                model="azure/chatgpt-v-3",
+                model="azure/gpt-4.1-nano",
                 messages=messages,
                 azure_ad_token="my-special-token",
             )
@@ -2497,7 +2497,7 @@ def test_completion_azure_key_completion_arg():
         litellm.set_verbose = True
         ## Test azure call
         response = completion(
-            model="azure/chatgpt-v-3",
+            model="azure/gpt-4.1-nano",
             messages=messages,
             api_key=old_key,
             logprobs=True,
@@ -2514,31 +2514,6 @@ def test_completion_azure_key_completion_arg():
         pytest.fail(f"Error occurred: {e}")
 
 
-# test_completion_azure_key_completion_arg()
-
-
-def test_azure_instruct():
-    litellm.set_verbose = True
-    response = completion(
-        model="azure_text/instruct-model",
-        messages=[{"role": "user", "content": "What is the weather like in Boston?"}],
-        max_tokens=10,
-    )
-    print("response", response)
-
-
-@pytest.mark.asyncio
-async def test_azure_instruct_stream():
-    litellm.set_verbose = False
-    response = await litellm.acompletion(
-        model="azure_text/instruct-model",
-        messages=[{"role": "user", "content": "What is the weather like in Boston?"}],
-        max_tokens=10,
-        stream=True,
-    )
-    print("response", response)
-    async for chunk in response:
-        print(chunk)
 
 
 async def test_re_use_azure_async_client():
@@ -2555,7 +2530,7 @@ async def test_re_use_azure_async_client():
         ## Test azure call
         for _ in range(3):
             response = await litellm.acompletion(
-                model="azure/chatgpt-v-3", messages=messages, client=client
+                model="azure/gpt-4.1-nano", messages=messages, client=client
             )
             print(f"response: {response}")
     except Exception as e:
@@ -2581,37 +2556,6 @@ def test_re_use_openaiClient():
         pytest.fail("got Exception", e)
 
 
-def test_completion_azure():
-    try:
-        litellm.set_verbose = False
-        ## Test azure call
-        response = completion(
-            model="azure/gpt-4o-new-test",
-            messages=messages,
-            api_key="os.environ/AZURE_API_KEY",
-        )
-        print(f"response: {response}")
-        print(f"response hidden params: {response._hidden_params}")
-        ## Test azure flag for backwards-compat
-        # response = completion(
-        #     model="chatgpt-v-3",
-        #     messages=messages,
-        #     azure=True,
-        #     max_tokens=10
-        # )
-        # Add any assertions here to check the response
-        print(response)
-
-        cost = completion_cost(completion_response=response)
-        assert cost > 0.0
-        print("Cost for azure completion request", cost)
-    except Exception as e:
-        pytest.fail(f"Error occurred: {e}")
-
-
-# test_completion_azure()
-
-
 @pytest.mark.skip(
     reason="this is bad test. It doesn't actually fail if the token is not set in the header. "
 )
@@ -2633,7 +2577,7 @@ def test_azure_openai_ad_token():
     litellm.input_callback = [tester]
     try:
         response = litellm.completion(
-            model="azure/chatgpt-v-3",  # e.g. gpt-35-instant
+            model="azure/gpt-4.1-nano",  # e.g. gpt-35-instant
             messages=[
                 {
                     "role": "user",
@@ -2671,7 +2615,7 @@ def test_completion_azure2():
 
         ## Test azure call
         response = completion(
-            model="azure/chatgpt-v-3",
+            model="azure/gpt-4.1-nano",
             messages=messages,
             api_base=api_base,
             api_key=api_key,
@@ -2708,7 +2652,7 @@ def test_completion_azure3():
 
         ## Test azure call
         response = completion(
-            model="azure/chatgpt-v-3",
+            model="azure/gpt-4.1-nano",
             messages=messages,
             max_tokens=10,
         )
@@ -2756,7 +2700,7 @@ def test_completion_azure_with_litellm_key():
         openai.api_key = "ymca"
 
         response = completion(
-            model="azure/chatgpt-v-3",
+            model="azure/gpt-4.1-nano",
             messages=messages,
         )
         # Add any assertions here to check the response
@@ -2784,7 +2728,7 @@ def test_completion_azure_deployment_id():
     try:
         litellm.set_verbose = True
         response = completion(
-            deployment_id="gpt-4o-new-test",
+            deployment_id="gpt-4.1-nano",
             model="gpt-3.5-turbo",
             messages=messages,
         )
@@ -3695,8 +3639,7 @@ def test_completion_volcengine():
     "model",
     [
         # "gemini-1.0-pro",
-        "gemini-1.5-pro",
-        # "gemini-2.5-flash-lite",
+        "gemini-2.5-flash-lite",
     ],
 )
 @pytest.mark.flaky(retries=3, delay=1)
@@ -4098,7 +4041,7 @@ async def test_completion_ai21_chat():
 
 @pytest.mark.parametrize(
     "model",
-    ["gpt-4o", "azure/chatgpt-v-3"],
+    ["gpt-4o", "azure/gpt-4.1-nano"],
 )
 @pytest.mark.parametrize(
     "stream",
@@ -4120,7 +4063,7 @@ def test_completion_response_ratelimit_headers(model, stream):
     assert "x-ratelimit-remaining-requests" in additional_headers
     assert "x-ratelimit-remaining-tokens" in additional_headers
 
-    if model == "azure/chatgpt-v-3":
+    if model == "azure/gpt-4.1-nano":
         # Azure OpenAI header
         assert "llm_provider-azureml-model-session" in additional_headers
     if model == "claude-3-sonnet-20240229":
diff --git a/tests/local_testing/test_completion_cost.py b/tests/local_testing/test_completion_cost.py
index 8b546b8427..9b2d747cfc 100644
--- a/tests/local_testing/test_completion_cost.py
+++ b/tests/local_testing/test_completion_cost.py
@@ -283,7 +283,7 @@ def test_cost_azure_embedding():
 
         async def _test():
             response = await litellm.aembedding(
-                model="azure/azure-embedding-model",
+                model="azure/text-embedding-ada-002",
                 input=["good morning from litellm", "gm"],
             )
 
diff --git a/tests/local_testing/test_config.py b/tests/local_testing/test_config.py
index b56be32881..a0a31c2e78 100644
--- a/tests/local_testing/test_config.py
+++ b/tests/local_testing/test_config.py
@@ -46,7 +46,7 @@ async def test_delete_deployment():
     import base64
 
     litellm_params = LiteLLM_Params(
-        model="azure/chatgpt-v-3",
+        model="azure/gpt-4.1-nano",
         api_key=os.getenv("AZURE_API_KEY"),
         api_base=os.getenv("AZURE_API_BASE"),
         api_version=os.getenv("AZURE_API_VERSION"),
@@ -232,7 +232,7 @@ async def test_db_error_new_model_check():
 
 
 litellm_params = LiteLLM_Params(
-    model="azure/chatgpt-v-3",
+    model="azure/gpt-4.1-nano",
     api_key=os.getenv("AZURE_API_KEY"),
     api_base=os.getenv("AZURE_API_BASE"),
     api_version=os.getenv("AZURE_API_VERSION"),
@@ -250,7 +250,7 @@ def _create_model_list(flag_value: Literal[0, 1], master_key: str):
     import base64
 
     new_litellm_params = LiteLLM_Params(
-        model="azure/chatgpt-v-3-3",
+        model="azure/gpt-4.1-nano-3",
         api_key=os.getenv("AZURE_API_KEY"),
         api_base=os.getenv("AZURE_API_BASE"),
         api_version=os.getenv("AZURE_API_VERSION"),
diff --git a/tests/local_testing/test_configs/test_bad_config.yaml b/tests/local_testing/test_configs/test_bad_config.yaml
index 0a16ecb3c5..ae699fdecb 100644
--- a/tests/local_testing/test_configs/test_bad_config.yaml
+++ b/tests/local_testing/test_configs/test_bad_config.yaml
@@ -5,17 +5,17 @@ model_list:
       model: gpt-3.5-turbo
   - model_name: working-azure-gpt-3.5-turbo
     litellm_params:
-      model: azure/chatgpt-v-3
+      model: azure/gpt-4.1-nano
       api_base: os.environ/AZURE_API_BASE
       api_key: os.environ/AZURE_API_KEY
   - model_name: azure-gpt-3.5-turbo
     litellm_params:
-      model: azure/chatgpt-v-3
+      model: azure/gpt-4.1-nano
       api_base: os.environ/AZURE_API_BASE
       api_key: bad-key
   - model_name: azure-embedding
     litellm_params:
-      model: azure/azure-embedding-model
+      model: azure/text-embedding-ada-002
       api_base: os.environ/AZURE_API_BASE
       api_key: bad-key
     
\ No newline at end of file
diff --git a/tests/local_testing/test_configs/test_cloudflare_azure_with_cache_config.yaml b/tests/local_testing/test_configs/test_cloudflare_azure_with_cache_config.yaml
index aeadbeb872..acb0785850 100644
--- a/tests/local_testing/test_configs/test_cloudflare_azure_with_cache_config.yaml
+++ b/tests/local_testing/test_configs/test_cloudflare_azure_with_cache_config.yaml
@@ -1,7 +1,7 @@
 model_list:
   - model_name: azure-cloudflare
     litellm_params:
-      model: azure/chatgpt-v-3
+      model: azure/gpt-4.1-nano
       api_base: https://gateway.ai.cloudflare.com/v1/0399b10e77ac6668c80404a5ff49eb37/litellm-test/azure-openai/openai-gpt-4-test-v-1
       api_key: os.environ/AZURE_API_KEY
       api_version: 2023-07-01-preview
diff --git a/tests/local_testing/test_configs/test_config_no_auth.yaml b/tests/local_testing/test_configs/test_config_no_auth.yaml
index 075bf7a09d..1ddfee464f 100644
--- a/tests/local_testing/test_configs/test_config_no_auth.yaml
+++ b/tests/local_testing/test_configs/test_config_no_auth.yaml
@@ -12,7 +12,7 @@ model_list:
 - litellm_params:
     api_base: https://gateway.ai.cloudflare.com/v1/0399b10e77ac6668c80404a5ff49eb37/litellm-test/azure-openai/openai-gpt-4-test-v-1
     api_key: os.environ/AZURE_API_KEY
-    model: azure/chatgpt-v-3
+    model: azure/gpt-4.1-nano
   model_name: azure-cloudflare-model
 - litellm_params:
     api_base: https://openai-france-1234.openai.azure.com
@@ -52,7 +52,7 @@ model_list:
     api_base: os.environ/AZURE_API_BASE
     api_key: os.environ/AZURE_API_KEY
     api_version: 2023-07-01-preview
-    model: azure/azure-embedding-model
+    model: azure/text-embedding-ada-002
   model_info:
     mode: embedding
   model_name: azure-embedding-model
@@ -105,7 +105,7 @@ model_list:
     api_base: os.environ/AZURE_API_BASE
     api_key: os.environ/AZURE_API_KEY
     api_version: 2023-07-01-preview
-    model: azure/azure-embedding-model
+    model: azure/text-embedding-ada-002
   model_info:
     base_model: text-embedding-ada-002
     mode: embedding
diff --git a/tests/local_testing/test_configs/test_custom_logger.yaml b/tests/local_testing/test_configs/test_custom_logger.yaml
index 2ad500b36f..eb3d623f47 100644
--- a/tests/local_testing/test_configs/test_custom_logger.yaml
+++ b/tests/local_testing/test_configs/test_custom_logger.yaml
@@ -1,7 +1,7 @@
 model_list: 
   - model_name: Azure OpenAI GPT-4 Canada
     litellm_params:
-      model: azure/chatgpt-v-3
+      model: azure/gpt-4.1-nano
       api_base: os.environ/AZURE_API_BASE
       api_key: os.environ/AZURE_API_KEY
       api_version: "2023-07-01-preview"
@@ -11,7 +11,7 @@ model_list:
       id: gm
   - model_name: azure-embedding-model
     litellm_params:
-      model: azure/azure-embedding-model
+      model: azure/text-embedding-ada-002
       api_base: os.environ/AZURE_API_BASE
       api_key: os.environ/AZURE_API_KEY
       api_version: "2023-07-01-preview"
diff --git a/tests/local_testing/test_custom_callback_input.py b/tests/local_testing/test_custom_callback_input.py
index 90e9adb789..b3c81b9906 100644
--- a/tests/local_testing/test_custom_callback_input.py
+++ b/tests/local_testing/test_custom_callback_input.py
@@ -450,12 +450,12 @@ def test_chat_azure_stream():
         customHandler = CompletionCustomHandler()
         litellm.callbacks = [customHandler]
         response = litellm.completion(
-            model="azure/gpt-4o-new-test",
+            model="azure/gpt-4.1-nano",
             messages=[{"role": "user", "content": "Hi 👋 - i'm sync azure"}],
         )
         # test streaming
         response = litellm.completion(
-            model="azure/gpt-4o-new-test",
+            model="azure/gpt-4.1-nano",
             messages=[{"role": "user", "content": "Hi 👋 - i'm sync azure"}],
             stream=True,
         )
@@ -464,7 +464,7 @@ def test_chat_azure_stream():
         # test failure callback
         try:
             response = litellm.completion(
-                model="azure/gpt-4o-new-test",
+                model="azure/gpt-4.1-nano",
                 messages=[{"role": "user", "content": "Hi 👋 - i'm sync azure"}],
                 api_key="my-bad-key",
                 stream=True,
@@ -491,12 +491,12 @@ async def test_async_chat_azure_stream():
         customHandler = CompletionCustomHandler()
         litellm.callbacks = [customHandler]
         response = await litellm.acompletion(
-            model="azure/chatgpt-v-3",
+            model="azure/gpt-4.1-nano",
             messages=[{"role": "user", "content": "Hi 👋 - i'm async azure"}],
         )
         ## test streaming
         response = await litellm.acompletion(
-            model="azure/chatgpt-v-3",
+            model="azure/gpt-4.1-nano",
             messages=[{"role": "user", "content": "Hi 👋 - i'm async azure"}],
             stream=True,
         )
@@ -507,7 +507,7 @@ async def test_async_chat_azure_stream():
         # test failure callback
         try:
             response = await litellm.acompletion(
-                model="azure/chatgpt-v-3",
+                model="azure/gpt-4.1-nano",
                 messages=[{"role": "user", "content": "Hi 👋 - i'm async azure"}],
                 api_key="my-bad-key",
                 stream=True,
@@ -774,7 +774,8 @@ async def test_async_embedding_openai():
         customHandler_failure = CompletionCustomHandler()
         litellm.callbacks = [customHandler_success]
         response = await litellm.aembedding(
-            model="azure/azure-embedding-model", input=["good morning from litellm"]
+            model="text-embedding-ada-002",
+            input=["good morning from litellm"],
         )
         await asyncio.sleep(1)
         print(f"customHandler_success.errors: {customHandler_success.errors}")
@@ -811,7 +812,7 @@ def test_amazing_sync_embedding():
         customHandler_failure = CompletionCustomHandler()
         litellm.callbacks = [customHandler_success]
         response = litellm.embedding(
-            model="azure/azure-embedding-model", input=["good morning from litellm"]
+            model="azure/text-embedding-ada-002", input=["good morning from litellm"]
         )
         print(f"customHandler_success.errors: {customHandler_success.errors}")
         print(f"customHandler_success.states: {customHandler_success.states}")
@@ -823,7 +824,7 @@ def test_amazing_sync_embedding():
         litellm.callbacks = [customHandler_failure]
         try:
             response = litellm.embedding(
-                model="azure/azure-embedding-model",
+                model="azure/text-embedding-ada-002",
                 input=["good morning from litellm"],
                 api_key="my-bad-key",
             )
@@ -846,7 +847,7 @@ async def test_async_embedding_azure():
         customHandler_failure = CompletionCustomHandler()
         litellm.callbacks = [customHandler_success]
         response = await litellm.aembedding(
-            model="azure/azure-embedding-model", input=["good morning from litellm"]
+            model="azure/text-embedding-ada-002", input=["good morning from litellm"]
         )
         await asyncio.sleep(1)
         print(f"customHandler_success.errors: {customHandler_success.errors}")
@@ -858,7 +859,7 @@ async def test_async_embedding_azure():
         litellm.callbacks = [customHandler_failure]
         try:
             response = await litellm.aembedding(
-                model="azure/azure-embedding-model",
+                model="azure/text-embedding-ada-002",
                 input=["good morning from litellm"],
                 api_key="my-bad-key",
             )
@@ -914,7 +915,6 @@ async def test_async_embedding_bedrock():
         pytest.fail(f"An exception occurred: {str(e)}")
 
 
-
 # Image Generation
 
 
@@ -1004,7 +1004,7 @@ def test_turn_off_message_logging():
     "model",
     [
         "ft:gpt-3.5-turbo:my-org:custom_suffix:id"
-    ],  # "gpt-3.5-turbo", "azure/chatgpt-v-3",
+    ],  # "gpt-3.5-turbo", "azure/gpt-4.1-nano",
 )
 @pytest.mark.parametrize(
     "turn_off_message_logging",
diff --git a/tests/local_testing/test_custom_logger.py b/tests/local_testing/test_custom_logger.py
index ee212658c8..baba409368 100644
--- a/tests/local_testing/test_custom_logger.py
+++ b/tests/local_testing/test_custom_logger.py
@@ -160,7 +160,7 @@ def test_completion_azure_stream_moderation_failure():
         ]
         try:
             response = completion(
-                model="azure/chatgpt-v-3",
+                model="azure/gpt-4.1-nano",
                 messages=messages,
                 mock_response="Exception: content_filter_policy",
                 stream=True,
@@ -195,7 +195,7 @@ def test_async_custom_handler_stream():
         async def test_1():
             nonlocal complete_streaming_response
             response = await litellm.acompletion(
-                model="azure/chatgpt-v-3", messages=messages, stream=True
+                model="azure/gpt-4.1-nano", messages=messages, stream=True
             )
             async for chunk in response:
                 complete_streaming_response += (
@@ -239,7 +239,7 @@ def test_azure_completion_stream():
         complete_streaming_response = ""
 
         response = litellm.completion(
-            model="azure/chatgpt-v-3", messages=messages, stream=True
+            model="azure/gpt-4.1-nano", messages=messages, stream=True
         )
         for chunk in response:
             complete_streaming_response += chunk["choices"][0]["delta"]["content"] or ""
diff --git a/tests/local_testing/test_embedding.py b/tests/local_testing/test_embedding.py
index 06f8346994..60e2a0764b 100644
--- a/tests/local_testing/test_embedding.py
+++ b/tests/local_testing/test_embedding.py
@@ -107,7 +107,7 @@ def test_openai_embedding_3():
 @pytest.mark.parametrize(
     "model, api_base, api_key",
     [
-        # ("azure/azure-embedding-model", None, None),
+        # ("azure/text-embedding-ada-002", None, None),
         ("together_ai/togethercomputer/m2-bert-80M-8k-retrieval", None, None),
     ],
 )
@@ -253,7 +253,7 @@ async def test_azure_ai_embedding_image(model, api_base, api_key, sync_mode):
 def test_openai_azure_embedding_timeouts():
     try:
         response = embedding(
-            model="azure/azure-embedding-model",
+            model="azure/text-embedding-ada-002",
             input=["good morning from litellm"],
             timeout=0.00001,
         )
@@ -301,7 +301,7 @@ def test_openai_azure_embedding():
         os.environ["AZURE_API_KEY"] = ""
 
         response = embedding(
-            model="azure/azure-embedding-model",
+            model="azure/text-embedding-ada-002",
             input=["good morning from litellm", "this is another item"],
             api_key=api_key,
             api_base=api_base,
@@ -380,8 +380,16 @@ def test_openai_azure_embedding_optional_arg():
             azure_ad_token="test",
         )
 
-        assert mock_client.called_once_with(model="test", input=["test"], timeout=600)
+        mock_client.assert_called_once_with(
+            model="test", 
+            input=["test"], 
+            extra_body={"azure_ad_token": "test"}, 
+            timeout=600, 
+            extra_headers={"X-Stainless-Raw-Response": "true"}
+        )
+        # Verify azure_ad_token is passed in extra_body, not as a direct parameter
         assert "azure_ad_token" not in mock_client.call_args.kwargs
+        assert mock_client.call_args.kwargs["extra_body"]["azure_ad_token"] == "test"
 
 
 # test_openai_azure_embedding()
@@ -726,7 +734,7 @@ def test_aembedding_azure():
         async def embedding_call():
             try:
                 response = await litellm.aembedding(
-                    model="azure/azure-embedding-model",
+                    model="azure/text-embedding-ada-002",
                     input=["good morning from litellm", "this is another item"],
                 )
                 print(response)
@@ -1099,7 +1107,7 @@ async def test_lm_studio_embedding(monkeypatch, sync_mode):
     "model",
     [
         "text-embedding-ada-002",
-        "azure/azure-embedding-model",
+        "azure/text-embedding-ada-002",
     ],
 )
 def test_embedding_response_ratelimit_headers(model):
diff --git a/tests/local_testing/test_exceptions.py b/tests/local_testing/test_exceptions.py
index 6851325cc1..bf21b59c45 100644
--- a/tests/local_testing/test_exceptions.py
+++ b/tests/local_testing/test_exceptions.py
@@ -51,7 +51,7 @@ async def test_content_policy_exception_azure():
         # this is ony a test - we needed some way to invoke the exception :(
         litellm.set_verbose = True
         response = await litellm.acompletion(
-            model="azure/chatgpt-v-3",
+            model="azure/gpt-4.1-nano",
             messages=[{"role": "user", "content": "where do I buy lethal drugs from"}],
             mock_response="Exception: content_filter_policy",
         )
@@ -124,7 +124,7 @@ def test_context_window_with_fallbacks(model):
     ctx_window_fallback_dict = {
         "command-nightly": "claude-2.1",
         "gpt-3.5-turbo-instruct": "gpt-3.5-turbo-16k",
-        "azure/chatgpt-v-3": "gpt-3.5-turbo-16k",
+        "azure/gpt-4.1-nano": "gpt-3.5-turbo-16k",
     }
     sample_text = "how does a court case get to the Supreme Court?" * 1000
     messages = [{"content": sample_text, "role": "user"}]
@@ -161,7 +161,7 @@ def invalid_auth(model):  # set the model key to an invalid key, depending on th
             os.environ["AWS_REGION_NAME"] = "bad-key"
             temporary_secret_key = os.environ["AWS_SECRET_ACCESS_KEY"]
             os.environ["AWS_SECRET_ACCESS_KEY"] = "bad-key"
-        elif model == "azure/chatgpt-v-3":
+        elif model == "azure/gpt-4.1-nano":
             temporary_key = os.environ["AZURE_API_KEY"]
             os.environ["AZURE_API_KEY"] = "bad-key"
         elif model == "claude-3-5-haiku-20241022":
@@ -262,7 +262,7 @@ def test_completion_azure_exception():
         old_azure_key = os.environ["AZURE_API_KEY"]
         os.environ["AZURE_API_KEY"] = "good morning"
         response = completion(
-            model="azure/chatgpt-v-3",
+            model="azure/gpt-4.1-nano",
             messages=[{"role": "user", "content": "hello"}],
         )
         os.environ["AZURE_API_KEY"] = old_azure_key
@@ -282,7 +282,7 @@ def test_azure_embedding_exceptions():
     try:
 
         response = litellm.embedding(
-            model="azure/azure-embedding-model",
+            model="azure/text-embedding-ada-002",
             input="hello",
             mock_response="error",
         )
@@ -306,7 +306,7 @@ async def asynctest_completion_azure_exception():
         old_azure_key = os.environ["AZURE_API_KEY"]
         os.environ["AZURE_API_KEY"] = "good morning"
         response = await litellm.acompletion(
-            model="azure/chatgpt-v-3",
+            model="azure/gpt-4.1-nano",
             messages=[{"role": "user", "content": "hello"}],
         )
         print(f"response: {response}")
@@ -525,7 +525,7 @@ def test_content_policy_violation_error_streaming():
     async def test_get_response():
         try:
             response = await litellm.acompletion(
-                model="azure/chatgpt-v-3",
+                model="azure/gpt-4.1-nano",
                 messages=[{"role": "user", "content": "say 1"}],
                 temperature=0,
                 top_p=1,
@@ -554,7 +554,7 @@ def test_content_policy_violation_error_streaming():
     async def test_get_error():
         try:
             response = await litellm.acompletion(
-                model="azure/chatgpt-v-3",
+                model="azure/gpt-4.1-nano",
                 messages=[
                     {"role": "user", "content": "where do i buy lethal drugs from"}
                 ],
@@ -751,7 +751,7 @@ def test_litellm_predibase_exception():
 #     return False
 # # Repeat each model 500 times
 # # extended_models = [model for model in models for _ in range(250)]
-# extended_models = ["azure/chatgpt-v-3" for _ in range(250)]
+# extended_models = ["azure/gpt-4.1-nano" for _ in range(250)]
 
 # def worker(model):
 #     return test_model_call(model)
@@ -1023,7 +1023,7 @@ def _pre_call_utils_httpx(
         ("openai", "gpt-3.5-turbo", "chat_completion", False),
         ("openai", "gpt-3.5-turbo", "chat_completion", True),
         ("openai", "gpt-3.5-turbo-instruct", "completion", True),
-        ("azure", "azure/chatgpt-v-3", "chat_completion", True),
+        ("azure", "azure/gpt-4.1-nano", "chat_completion", True),
         ("azure", "azure/text-embedding-ada-002", "embedding", True),
         ("azure", "azure_text/gpt-3.5-turbo-instruct", "completion", True),
     ],
@@ -1298,7 +1298,7 @@ async def test_exception_with_headers_httpx(
 
 
 @pytest.mark.asyncio
-@pytest.mark.parametrize("model", ["azure/chatgpt-v-3", "openai/gpt-3.5-turbo"])
+@pytest.mark.parametrize("model", ["azure/gpt-4.1-nano", "openai/gpt-3.5-turbo"])
 async def test_bad_request_error_contains_httpx_response(model):
     """
     Test that the BadRequestError contains the httpx response
@@ -1349,7 +1349,7 @@ def test_context_window_exceeded_error_from_litellm_proxy():
 
 @pytest.mark.parametrize("sync_mode", [True, False])
 @pytest.mark.parametrize("stream_mode", [True, False])
-@pytest.mark.parametrize("model", ["azure/gpt-4o-new-test"])  # "gpt-4o-mini",
+@pytest.mark.parametrize("model", ["gpt-4.1-nano"])  # "gpt-4o-mini",
 @pytest.mark.asyncio
 async def test_exception_bubbling_up(sync_mode, stream_mode, model):
     """
diff --git a/tests/local_testing/test_function_calling.py b/tests/local_testing/test_function_calling.py
index 71249898f7..1312c4dcc6 100644
--- a/tests/local_testing/test_function_calling.py
+++ b/tests/local_testing/test_function_calling.py
@@ -48,7 +48,7 @@ def get_current_weather(location, unit="fahrenheit"):
         "gpt-3.5-turbo-1106",
         "mistral/mistral-large-latest",
         "claude-3-haiku-20240307",
-        "gemini/gemini-1.5-pro",
+        "gemini/gemini-2.5-flash-lite",
         "anthropic.claude-3-sonnet-20240229-v1:0",
         "cohere_chat/command-r",
     ],
diff --git a/tests/local_testing/test_gcs_bucket.py b/tests/local_testing/test_gcs_bucket.py
index 5a9c99108c..284efce528 100644
--- a/tests/local_testing/test_gcs_bucket.py
+++ b/tests/local_testing/test_gcs_bucket.py
@@ -108,7 +108,7 @@ async def test_aaabasic_gcs_logger():
             },
             "endpoint": "http://localhost:4000/chat/completions",
             "model_group": "gpt-3.5-turbo",
-            "deployment": "azure/chatgpt-v-3",
+            "deployment": "azure/gpt-4.1-nano",
             "model_info": {
                 "id": "4bad40a1eb6bebd1682800f16f44b9f06c52a6703444c99c7f9f32e9de3693b4",
                 "db_model": False,
@@ -216,7 +216,7 @@ async def test_basic_gcs_logger_failure():
                 },
                 "endpoint": "http://localhost:4000/chat/completions",
                 "model_group": "gpt-3.5-turbo",
-                "deployment": "azure/chatgpt-v-3",
+                "deployment": "azure/gpt-4.1-nano",
                 "model_info": {
                     "id": "4bad40a1eb6bebd1682800f16f44b9f06c52a6703444c99c7f9f32e9de3693b4",
                     "db_model": False,
@@ -626,7 +626,7 @@ async def test_basic_gcs_logger_with_folder_in_bucket_name():
             },
             "endpoint": "http://localhost:4000/chat/completions",
             "model_group": "gpt-3.5-turbo",
-            "deployment": "azure/chatgpt-v-3",
+            "deployment": "azure/gpt-4.1-nano",
             "model_info": {
                 "id": "4bad40a1eb6bebd1682800f16f44b9f06c52a6703444c99c7f9f32e9de3693b4",
                 "db_model": False,
diff --git a/tests/local_testing/test_helicone_integration.py b/tests/local_testing/test_helicone_integration.py
index 4d77058598..a128dd84c8 100644
--- a/tests/local_testing/test_helicone_integration.py
+++ b/tests/local_testing/test_helicone_integration.py
@@ -78,7 +78,7 @@ async def make_async_calls(metadata=None, **completion_kwargs):
 
 def create_async_task(**completion_kwargs):
     completion_args = {
-        "model": "azure/chatgpt-v-3",
+        "model": "azure/gpt-4.1-nano",
         "api_version": "2024-02-01",
         "messages": [{"role": "user", "content": "This is a test"}],
         "max_tokens": 5,
diff --git a/tests/local_testing/test_least_busy_routing.py b/tests/local_testing/test_least_busy_routing.py
index 7e4393da0b..5a2fa19562 100644
--- a/tests/local_testing/test_least_busy_routing.py
+++ b/tests/local_testing/test_least_busy_routing.py
@@ -33,7 +33,7 @@ def test_model_added():
         "litellm_params": {
             "metadata": {
                 "model_group": "gpt-3.5-turbo",
-                "deployment": "azure/chatgpt-v-3",
+                "deployment": "azure/gpt-4.1-nano",
             },
             "model_info": {"id": "1234"},
         }
@@ -47,7 +47,7 @@ def test_get_available_deployments():
     test_cache = DualCache()
     least_busy_logger = LeastBusyLoggingHandler(router_cache=test_cache, model_list=[])
     model_group = "gpt-3.5-turbo"
-    deployment = "azure/chatgpt-v-3"
+    deployment = "azure/gpt-4.1-nano"
     kwargs = {
         "litellm_params": {
             "metadata": {
@@ -113,7 +113,7 @@ async def test_router_get_available_deployments(async_test):
     router.leastbusy_logger.test_flag = True
 
     model_group = "azure-model"
-    deployment = "azure/chatgpt-v-3"
+    deployment = "azure/gpt-4.1-nano"
     request_count_dict = {1: 10, 2: 54, 3: 100}
     cache_key = f"{model_group}_request_count"
     if async_test is True:
diff --git a/tests/local_testing/test_load_test_router_s3.py b/tests/local_testing/test_load_test_router_s3.py
index 3a2567b686..6b0b605061 100644
--- a/tests/local_testing/test_load_test_router_s3.py
+++ b/tests/local_testing/test_load_test_router_s3.py
@@ -46,7 +46,7 @@
 #         {
 #             "model_name": "gpt-3.5-turbo",
 #             "litellm_params": {
-#                 "model": "azure/chatgpt-v-3",
+#                 "model": "azure/gpt-4.1-nano",
 #                 "api_key": os.getenv("AZURE_API_KEY"),
 #                 "api_base": os.getenv("AZURE_API_BASE"),
 #                 "api_version": os.getenv("AZURE_API_VERSION"),
diff --git a/tests/local_testing/test_loadtest_router.py b/tests/local_testing/test_loadtest_router.py
index 0d8a09ca62..14f6cf627a 100644
--- a/tests/local_testing/test_loadtest_router.py
+++ b/tests/local_testing/test_loadtest_router.py
@@ -38,7 +38,7 @@
 #         {
 #             "model_name": "gpt-3.5-turbo",
 #             "litellm_params": {
-#                 "model": "azure/chatgpt-v-3",
+#                 "model": "azure/gpt-4.1-nano",
 #                 "api_key": os.getenv("AZURE_API_KEY"),
 #                 "api_base": os.getenv("AZURE_API_BASE"),
 #                 "api_version": os.getenv("AZURE_API_VERSION"),
diff --git a/tests/local_testing/test_lowest_cost_routing.py b/tests/local_testing/test_lowest_cost_routing.py
index c0305f33fc..bad8bbbb0a 100644
--- a/tests/local_testing/test_lowest_cost_routing.py
+++ b/tests/local_testing/test_lowest_cost_routing.py
@@ -60,7 +60,7 @@ async def test_get_available_deployments_custom_price():
         {
             "model_name": "gpt-3.5-turbo",
             "litellm_params": {
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "input_cost_per_token": 0.00003,
                 "output_cost_per_token": 0.00003,
             },
diff --git a/tests/local_testing/test_lowest_latency_routing.py b/tests/local_testing/test_lowest_latency_routing.py
index 74dae25c1f..2a7b0eadc4 100644
--- a/tests/local_testing/test_lowest_latency_routing.py
+++ b/tests/local_testing/test_lowest_latency_routing.py
@@ -48,7 +48,7 @@ async def test_latency_memory_leak(sync_mode):
         "litellm_params": {
             "metadata": {
                 "model_group": "gpt-3.5-turbo",
-                "deployment": "azure/chatgpt-v-3",
+                "deployment": "azure/gpt-4.1-nano",
             },
             "model_info": {"id": deployment_id},
         }
@@ -130,7 +130,7 @@ def test_latency_updated():
         "litellm_params": {
             "metadata": {
                 "model_group": "gpt-3.5-turbo",
-                "deployment": "azure/chatgpt-v-3",
+                "deployment": "azure/gpt-4.1-nano",
             },
             "model_info": {"id": deployment_id},
         }
@@ -173,7 +173,7 @@ def test_latency_updated_custom_ttl():
         "litellm_params": {
             "metadata": {
                 "model_group": "gpt-3.5-turbo",
-                "deployment": "azure/chatgpt-v-3",
+                "deployment": "azure/gpt-4.1-nano",
             },
             "model_info": {"id": deployment_id},
         }
@@ -200,12 +200,12 @@ def test_get_available_deployments():
     model_list = [
         {
             "model_name": "gpt-3.5-turbo",
-            "litellm_params": {"model": "azure/chatgpt-v-3"},
+            "litellm_params": {"model": "azure/gpt-4.1-nano"},
             "model_info": {"id": "1234"},
         },
         {
             "model_name": "gpt-3.5-turbo",
-            "litellm_params": {"model": "azure/chatgpt-v-3"},
+            "litellm_params": {"model": "azure/gpt-4.1-nano"},
             "model_info": {"id": "5678"},
         },
     ]
@@ -219,7 +219,7 @@ def test_get_available_deployments():
         "litellm_params": {
             "metadata": {
                 "model_group": "gpt-3.5-turbo",
-                "deployment": "azure/chatgpt-v-3",
+                "deployment": "azure/gpt-4.1-nano",
             },
             "model_info": {"id": deployment_id},
         }
@@ -240,7 +240,7 @@ def test_get_available_deployments():
         "litellm_params": {
             "metadata": {
                 "model_group": "gpt-3.5-turbo",
-                "deployment": "azure/chatgpt-v-3",
+                "deployment": "azure/gpt-4.1-nano",
             },
             "model_info": {"id": deployment_id},
         }
@@ -275,7 +275,7 @@ async def _deploy(lowest_latency_logger, deployment_id, tokens_used, duration):
         "litellm_params": {
             "metadata": {
                 "model_group": "gpt-3.5-turbo",
-                "deployment": "azure/chatgpt-v-3",
+                "deployment": "azure/gpt-4.1-nano",
             },
             "model_info": {"id": deployment_id},
         }
@@ -317,12 +317,12 @@ def test_get_available_endpoints_tpm_rpm_check_async(ans_rpm):
     model_list = [
         {
             "model_name": "gpt-3.5-turbo",
-            "litellm_params": {"model": "azure/chatgpt-v-3"},
+            "litellm_params": {"model": "azure/gpt-4.1-nano"},
             "model_info": {"id": "1234", "rpm": ans_rpm},
         },
         {
             "model_name": "gpt-3.5-turbo",
-            "litellm_params": {"model": "azure/chatgpt-v-3"},
+            "litellm_params": {"model": "azure/gpt-4.1-nano"},
             "model_info": {"id": "5678", "rpm": non_ans_rpm},
         },
     ]
@@ -366,12 +366,12 @@ def test_get_available_endpoints_tpm_rpm_check(ans_rpm):
     model_list = [
         {
             "model_name": "gpt-3.5-turbo",
-            "litellm_params": {"model": "azure/chatgpt-v-3"},
+            "litellm_params": {"model": "azure/gpt-4.1-nano"},
             "model_info": {"id": "1234", "rpm": ans_rpm},
         },
         {
             "model_name": "gpt-3.5-turbo",
-            "litellm_params": {"model": "azure/chatgpt-v-3"},
+            "litellm_params": {"model": "azure/gpt-4.1-nano"},
             "model_info": {"id": "5678", "rpm": non_ans_rpm},
         },
     ]
@@ -385,7 +385,7 @@ def test_get_available_endpoints_tpm_rpm_check(ans_rpm):
         "litellm_params": {
             "metadata": {
                 "model_group": "gpt-3.5-turbo",
-                "deployment": "azure/chatgpt-v-3",
+                "deployment": "azure/gpt-4.1-nano",
             },
             "model_info": {"id": deployment_id},
         }
@@ -407,7 +407,7 @@ def test_get_available_endpoints_tpm_rpm_check(ans_rpm):
         "litellm_params": {
             "metadata": {
                 "model_group": "gpt-3.5-turbo",
-                "deployment": "azure/chatgpt-v-3",
+                "deployment": "azure/gpt-4.1-nano",
             },
             "model_info": {"id": deployment_id},
         }
diff --git a/tests/local_testing/test_mem_usage.py b/tests/local_testing/test_mem_usage.py
index f91852899a..85ecd56878 100644
--- a/tests/local_testing/test_mem_usage.py
+++ b/tests/local_testing/test_mem_usage.py
@@ -29,7 +29,7 @@
 #     {
 #         "model_name": "gpt-3.5-turbo",  # openai model name
 #         "litellm_params": {  # params for litellm completion/embedding call
-#             "model": "azure/chatgpt-v-3",
+#             "model": "azure/gpt-4.1-nano",
 #             "api_key": os.getenv("AZURE_API_KEY"),
 #             "api_version": os.getenv("AZURE_API_VERSION"),
 #             "api_base": os.getenv("AZURE_API_BASE"),
@@ -40,7 +40,7 @@
 #     {
 #         "model_name": "bad-model",  # openai model name
 #         "litellm_params": {  # params for litellm completion/embedding call
-#             "model": "azure/chatgpt-v-3",
+#             "model": "azure/gpt-4.1-nano",
 #             "api_key": "bad-key",
 #             "api_version": os.getenv("AZURE_API_VERSION"),
 #             "api_base": os.getenv("AZURE_API_BASE"),
@@ -51,7 +51,7 @@
 #     {
 #         "model_name": "text-embedding-ada-002",
 #         "litellm_params": {
-#             "model": "azure/azure-embedding-model",
+#             "model": "azure/text-embedding-ada-002",
 #             "api_key": os.environ["AZURE_API_KEY"],
 #             "api_base": os.environ["AZURE_API_BASE"],
 #         },
diff --git a/tests/local_testing/test_mock_request.py b/tests/local_testing/test_mock_request.py
index de8764edba..f959ed8038 100644
--- a/tests/local_testing/test_mock_request.py
+++ b/tests/local_testing/test_mock_request.py
@@ -155,15 +155,14 @@ def test_router_mock_request_with_mock_timeout_with_fallbacks():
                 },
             },
             {
-                "model_name": "azure-gpt",
+                "model_name": "gpt-4.1-nano",
                 "litellm_params": {
-                    "model": "azure/gpt-4.1-nano",
-                    "api_key": os.getenv("AZURE_API_KEY"),
-                    "api_base": os.getenv("AZURE_API_BASE"),
+                    "model": "gpt-4.1-nano",
+                    "api_key": os.getenv("OPENAI_API_KEY"),
                 },
             },
         ],
-        fallbacks=[{"gpt-3.5-turbo": ["azure-gpt"]}],
+        fallbacks=[{"gpt-3.5-turbo": ["gpt-4.1-nano"]}],
     )
     response = router.completion(
         model="gpt-3.5-turbo",
@@ -176,5 +175,5 @@ def test_router_mock_request_with_mock_timeout_with_fallbacks():
     end_time = time.time()
     assert end_time - start_time >= 3, f"Time taken: {end_time - start_time}"
     assert (
-        "gpt-3.5-turbo-0125" in response.model
-    ), "Model should be azure gpt-3.5-turbo-0125"
+        "gpt-4.1-nano" in response.model
+    ), "Model should be gpt-4.1-nano"
diff --git a/tests/local_testing/test_prompt_injection_detection.py b/tests/local_testing/test_prompt_injection_detection.py
index 8443aadcc6..b438ebad7f 100644
--- a/tests/local_testing/test_prompt_injection_detection.py
+++ b/tests/local_testing/test_prompt_injection_detection.py
@@ -107,7 +107,7 @@ async def test_prompt_injection_llm_eval():
                 {
                     "model_name": "gpt-3.5-turbo",  # openai model name
                     "litellm_params": {  # params for litellm completion/embedding call
-                        "model": "azure/chatgpt-v-3",
+                        "model": "azure/gpt-4.1-nano",
                         "api_key": os.getenv("AZURE_API_KEY"),
                         "api_version": os.getenv("AZURE_API_VERSION"),
                         "api_base": os.getenv("AZURE_API_BASE"),
diff --git a/tests/local_testing/test_provider_specific_config.py b/tests/local_testing/test_provider_specific_config.py
index 8fc4c6ec21..f4df8995a9 100644
--- a/tests/local_testing/test_provider_specific_config.py
+++ b/tests/local_testing/test_provider_specific_config.py
@@ -729,7 +729,7 @@ def azure_openai_test_completion():
     try:
         # OVERRIDE WITH DYNAMIC MAX TOKENS
         response_1 = litellm.completion(
-            model="azure/chatgpt-v-3",
+            model="azure/gpt-4.1-nano",
             messages=[
                 {
                     "content": "Hello, how are you? Be as verbose as possible",
@@ -743,7 +743,7 @@ def azure_openai_test_completion():
 
         # USE CONFIG TOKENS
         response_2 = litellm.completion(
-            model="azure/chatgpt-v-3",
+            model="azure/gpt-4.1-nano",
             messages=[
                 {
                     "content": "Hello, how are you? Be as verbose as possible",
diff --git a/tests/local_testing/test_router.py b/tests/local_testing/test_router.py
index 4f9cf7aa95..a433ea500a 100644
--- a/tests/local_testing/test_router.py
+++ b/tests/local_testing/test_router.py
@@ -266,7 +266,7 @@ def test_router_sensitive_keys():
                 {
                     "model_name": "gpt-3.5-turbo",  # openai model name
                     "litellm_params": {  # params for litellm completion/embedding call
-                        "model": "azure/chatgpt-v-3",
+                        "model": "azure/gpt-4.1-nano",
                         "api_key": "special-key",
                     },
                     "model_info": {"id": 12345},
@@ -415,7 +415,7 @@ def test_exception_raising():
             {
                 "model_name": "gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": "bad-key",
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -527,7 +527,7 @@ def test_reading_key_from_model_list():
 def test_call_one_endpoint():
     # [PROD TEST CASE]
     # user passes one deployment they want to call on the router, we call the specified one
-    # this test makes a completion calls azure/chatgpt-v-3, it should work
+    # this test makes a completion calls azure/gpt-4.1-nano, it should work
     try:
         print("Testing calling a specific deployment")
         old_api_key = os.environ["AZURE_API_KEY"]
@@ -785,7 +785,7 @@ def test_router_context_window_check_pre_call_check_in_group_custom_model_info()
             {
                 "model_name": "gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -839,7 +839,7 @@ def test_router_context_window_check_pre_call_check():
             {
                 "model_name": "gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -893,7 +893,7 @@ def test_router_context_window_check_pre_call_check_out_group():
             {
                 "model_name": "gpt-3.5-turbo-small",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -972,7 +972,7 @@ def test_router_region_pre_call_check(allowed_model_region):
         {
             "model_name": "gpt-3.5-turbo",  # openai model name
             "litellm_params": {  # params for litellm completion/embedding call
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_key": os.getenv("AZURE_API_KEY"),
                 "api_version": os.getenv("AZURE_API_VERSION"),
                 "api_base": os.getenv("AZURE_API_BASE"),
@@ -2572,7 +2572,7 @@ def test_is_team_specific_model():
 #             {
 #                 "model_name": "gpt-3.5-turbo",
 #                 "litellm_params": {
-#                     "model": "azure/chatgpt-v-3",
+#                     "model": "azure/gpt-4.1-nano",
 #                     "api_key": os.getenv("AZURE_API_KEY"),
 #                     "api_base": os.getenv("AZURE_API_BASE"),
 #                     "tpm": 100000,
@@ -2582,7 +2582,7 @@ def test_is_team_specific_model():
 #             {
 #                 "model_name": "gpt-3.5-turbo",
 #                 "litellm_params": {
-#                     "model": "azure/chatgpt-v-3",
+#                     "model": "azure/gpt-4.1-nano",
 #                     "api_key": os.getenv("AZURE_API_KEY"),
 #                     "api_base": os.getenv("AZURE_API_BASE"),
 #                     "tpm": 500,
diff --git a/tests/local_testing/test_router_client_init.py b/tests/local_testing/test_router_client_init.py
index 42fc49a4c7..a0a274911d 100644
--- a/tests/local_testing/test_router_client_init.py
+++ b/tests/local_testing/test_router_client_init.py
@@ -43,7 +43,7 @@ async def test_router_init():
         {
             "model_name": "gpt-3.5-turbo",
             "litellm_params": {
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_key": os.getenv("AZURE_API_KEY"),
                 "api_base": os.getenv("AZURE_API_BASE"),
                 "api_version": os.getenv("AZURE_API_VERSION"),
diff --git a/tests/local_testing/test_router_debug_logs.py b/tests/local_testing/test_router_debug_logs.py
index bce09404d8..9b12aa13f0 100644
--- a/tests/local_testing/test_router_debug_logs.py
+++ b/tests/local_testing/test_router_debug_logs.py
@@ -33,7 +33,7 @@ def test_async_fallbacks(caplog):
         {
             "model_name": "azure/gpt-3.5-turbo",
             "litellm_params": {
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_key": os.getenv("AZURE_API_KEY"),
                 "api_version": os.getenv("AZURE_API_VERSION"),
                 "api_base": os.getenv("AZURE_API_BASE"),
@@ -93,7 +93,7 @@ def test_async_fallbacks(caplog):
     # - error request, falling back notice, success notice
     expected_logs = [
         "Falling back to model_group = azure/gpt-3.5-turbo",
-        "litellm.acompletion(model=azure/chatgpt-v-3)\x1b[32m 200 OK\x1b[0m",
+        "litellm.acompletion(model=azure/gpt-4.1-nano)\x1b[32m 200 OK\x1b[0m",
         "Successful fallback b/w models.",
     ]
 
diff --git a/tests/local_testing/test_router_fallbacks.py b/tests/local_testing/test_router_fallbacks.py
index 5b4e6ab580..52c37f4cb6 100644
--- a/tests/local_testing/test_router_fallbacks.py
+++ b/tests/local_testing/test_router_fallbacks.py
@@ -67,7 +67,7 @@ def test_sync_fallbacks():
             {  # list of model deployments
                 "model_name": "azure/gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": "bad-key",
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -78,7 +78,7 @@ def test_sync_fallbacks():
             {  # list of model deployments
                 "model_name": "azure/gpt-3.5-turbo-context-fallback",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -150,7 +150,7 @@ async def test_async_fallbacks():
         {  # list of model deployments
             "model_name": "azure/gpt-3.5-turbo",  # openai model name
             "litellm_params": {  # params for litellm completion/embedding call
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_key": "bad-key",
                 "api_version": os.getenv("AZURE_API_VERSION"),
                 "api_base": os.getenv("AZURE_API_BASE"),
@@ -161,7 +161,7 @@ async def test_async_fallbacks():
         {  # list of model deployments
             "model_name": "azure/gpt-3.5-turbo-context-fallback",  # openai model name
             "litellm_params": {  # params for litellm completion/embedding call
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_key": os.getenv("AZURE_API_KEY"),
                 "api_version": os.getenv("AZURE_API_VERSION"),
                 "api_base": os.getenv("AZURE_API_BASE"),
@@ -239,7 +239,7 @@ def test_sync_fallbacks_embeddings():
         {  # list of model deployments
             "model_name": "bad-azure-embedding-model",  # openai model name
             "litellm_params": {  # params for litellm completion/embedding call
-                "model": "azure/azure-embedding-model",
+                "model": "azure/text-embedding-ada-002",
                 "api_key": "bad-key",
                 "api_version": os.getenv("AZURE_API_VERSION"),
                 "api_base": os.getenv("AZURE_API_BASE"),
@@ -289,7 +289,7 @@ async def test_async_fallbacks_embeddings():
         {  # list of model deployments
             "model_name": "bad-azure-embedding-model",  # openai model name
             "litellm_params": {  # params for litellm completion/embedding call
-                "model": "azure/azure-embedding-model",
+                "model": "azure/text-embedding-ada-002",
                 "api_key": "bad-key",
                 "api_version": os.getenv("AZURE_API_VERSION"),
                 "api_base": os.getenv("AZURE_API_BASE"),
@@ -345,7 +345,7 @@ def test_dynamic_fallbacks_sync():
             {  # list of model deployments
                 "model_name": "azure/gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": "bad-key",
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -356,7 +356,7 @@ def test_dynamic_fallbacks_sync():
             {  # list of model deployments
                 "model_name": "azure/gpt-3.5-turbo-context-fallback",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -422,7 +422,7 @@ async def test_dynamic_fallbacks_async():
             {  # list of model deployments
                 "model_name": "azure/gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": "bad-key",
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -433,7 +433,7 @@ async def test_dynamic_fallbacks_async():
             {  # list of model deployments
                 "model_name": "azure/gpt-3.5-turbo-context-fallback",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -505,7 +505,7 @@ async def test_async_fallbacks_streaming():
         {  # list of model deployments
             "model_name": "azure/gpt-3.5-turbo",  # openai model name
             "litellm_params": {  # params for litellm completion/embedding call
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_key": "bad-key",
                 "api_version": os.getenv("AZURE_API_VERSION"),
                 "api_base": os.getenv("AZURE_API_BASE"),
@@ -516,7 +516,7 @@ async def test_async_fallbacks_streaming():
         {  # list of model deployments
             "model_name": "azure/gpt-3.5-turbo-context-fallback",  # openai model name
             "litellm_params": {  # params for litellm completion/embedding call
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_key": os.getenv("AZURE_API_KEY"),
                 "api_version": os.getenv("AZURE_API_VERSION"),
                 "api_base": os.getenv("AZURE_API_BASE"),
@@ -590,7 +590,7 @@ def test_sync_fallbacks_streaming():
             {  # list of model deployments
                 "model_name": "azure/gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": "bad-key",
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -601,7 +601,7 @@ def test_sync_fallbacks_streaming():
             {  # list of model deployments
                 "model_name": "azure/gpt-3.5-turbo-context-fallback",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -671,7 +671,7 @@ async def test_async_fallbacks_max_retries_per_request():
         {  # list of model deployments
             "model_name": "azure/gpt-3.5-turbo",  # openai model name
             "litellm_params": {  # params for litellm completion/embedding call
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_key": "bad-key",
                 "api_version": os.getenv("AZURE_API_VERSION"),
                 "api_base": os.getenv("AZURE_API_BASE"),
@@ -682,7 +682,7 @@ async def test_async_fallbacks_max_retries_per_request():
         {  # list of model deployments
             "model_name": "azure/gpt-3.5-turbo-context-fallback",  # openai model name
             "litellm_params": {  # params for litellm completion/embedding call
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_key": os.getenv("AZURE_API_KEY"),
                 "api_version": os.getenv("AZURE_API_VERSION"),
                 "api_base": os.getenv("AZURE_API_BASE"),
@@ -885,7 +885,7 @@ def test_custom_cooldown_times():
             {  # list of model deployments
                 "model_name": "gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": "bad-key",
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -895,7 +895,7 @@ def test_custom_cooldown_times():
             {  # list of model deployments
                 "model_name": "gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
diff --git a/tests/local_testing/test_router_get_deployments.py b/tests/local_testing/test_router_get_deployments.py
index 8d064be9a8..7fda8a2be2 100644
--- a/tests/local_testing/test_router_get_deployments.py
+++ b/tests/local_testing/test_router_get_deployments.py
@@ -41,7 +41,7 @@ def test_weighted_selection_router():
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_base": os.getenv("AZURE_API_BASE"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
@@ -54,7 +54,7 @@ def test_weighted_selection_router():
         )
         selection_counts = defaultdict(int)
 
-        # call get_available_deployment 1k times, it should pick azure/chatgpt-v-3 about 90% of the time
+        # call get_available_deployment 1k times, it should pick azure/gpt-4.1-nano about 90% of the time
         for _ in range(1000):
             selected_model = router.get_available_deployment("gpt-3.5-turbo")
             selected_model_id = selected_model["litellm_params"]["model"]
@@ -64,10 +64,10 @@ def test_weighted_selection_router():
 
         total_requests = sum(selection_counts.values())
 
-        # Assert that 'azure/chatgpt-v-3' has about 90% of the total requests
+        # Assert that 'azure/gpt-4.1-nano' has about 90% of the total requests
         assert (
-            selection_counts["azure/chatgpt-v-3"] / total_requests > 0.89
-        ), f"Assertion failed: 'azure/chatgpt-v-3' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}"
+            selection_counts["azure/gpt-4.1-nano"] / total_requests > 0.89
+        ), f"Assertion failed: 'azure/gpt-4.1-nano' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}"
 
         router.reset()
     except Exception as e:
@@ -97,7 +97,7 @@ def test_weighted_selection_router_tpm():
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_base": os.getenv("AZURE_API_BASE"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
@@ -110,7 +110,7 @@ def test_weighted_selection_router_tpm():
         )
         selection_counts = defaultdict(int)
 
-        # call get_available_deployment 1k times, it should pick azure/chatgpt-v-3 about 90% of the time
+        # call get_available_deployment 1k times, it should pick azure/gpt-4.1-nano about 90% of the time
         for _ in range(1000):
             selected_model = router.get_available_deployment("gpt-3.5-turbo")
             selected_model_id = selected_model["litellm_params"]["model"]
@@ -120,10 +120,10 @@ def test_weighted_selection_router_tpm():
 
         total_requests = sum(selection_counts.values())
 
-        # Assert that 'azure/chatgpt-v-3' has about 90% of the total requests
+        # Assert that 'azure/gpt-4.1-nano' has about 90% of the total requests
         assert (
-            selection_counts["azure/chatgpt-v-3"] / total_requests > 0.89
-        ), f"Assertion failed: 'azure/chatgpt-v-3' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}"
+            selection_counts["azure/gpt-4.1-nano"] / total_requests > 0.89
+        ), f"Assertion failed: 'azure/gpt-4.1-nano' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}"
 
         router.reset()
     except Exception as e:
@@ -153,7 +153,7 @@ def test_weighted_selection_router_tpm_as_router_param():
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_base": os.getenv("AZURE_API_BASE"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
@@ -166,7 +166,7 @@ def test_weighted_selection_router_tpm_as_router_param():
         )
         selection_counts = defaultdict(int)
 
-        # call get_available_deployment 1k times, it should pick azure/chatgpt-v-3 about 90% of the time
+        # call get_available_deployment 1k times, it should pick azure/gpt-4.1-nano about 90% of the time
         for _ in range(1000):
             selected_model = router.get_available_deployment("gpt-3.5-turbo")
             selected_model_id = selected_model["litellm_params"]["model"]
@@ -176,10 +176,10 @@ def test_weighted_selection_router_tpm_as_router_param():
 
         total_requests = sum(selection_counts.values())
 
-        # Assert that 'azure/chatgpt-v-3' has about 90% of the total requests
+        # Assert that 'azure/gpt-4.1-nano' has about 90% of the total requests
         assert (
-            selection_counts["azure/chatgpt-v-3"] / total_requests > 0.89
-        ), f"Assertion failed: 'azure/chatgpt-v-3' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}"
+            selection_counts["azure/gpt-4.1-nano"] / total_requests > 0.89
+        ), f"Assertion failed: 'azure/gpt-4.1-nano' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}"
 
         router.reset()
     except Exception as e:
@@ -210,7 +210,7 @@ def test_weighted_selection_router_rpm_as_router_param():
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_base": os.getenv("AZURE_API_BASE"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
@@ -224,7 +224,7 @@ def test_weighted_selection_router_rpm_as_router_param():
         )
         selection_counts = defaultdict(int)
 
-        # call get_available_deployment 1k times, it should pick azure/chatgpt-v-3 about 90% of the time
+        # call get_available_deployment 1k times, it should pick azure/gpt-4.1-nano about 90% of the time
         for _ in range(1000):
             selected_model = router.get_available_deployment("gpt-3.5-turbo")
             selected_model_id = selected_model["litellm_params"]["model"]
@@ -234,10 +234,10 @@ def test_weighted_selection_router_rpm_as_router_param():
 
         total_requests = sum(selection_counts.values())
 
-        # Assert that 'azure/chatgpt-v-3' has about 90% of the total requests
+        # Assert that 'azure/gpt-4.1-nano' has about 90% of the total requests
         assert (
-            selection_counts["azure/chatgpt-v-3"] / total_requests > 0.89
-        ), f"Assertion failed: 'azure/chatgpt-v-3' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}"
+            selection_counts["azure/gpt-4.1-nano"] / total_requests > 0.89
+        ), f"Assertion failed: 'azure/gpt-4.1-nano' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}"
 
         router.reset()
     except Exception as e:
@@ -266,7 +266,7 @@ def test_weighted_selection_router_no_rpm_set():
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_base": os.getenv("AZURE_API_BASE"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
@@ -286,7 +286,7 @@ def test_weighted_selection_router_no_rpm_set():
         )
         selection_counts = defaultdict(int)
 
-        # call get_available_deployment 1k times, it should pick azure/chatgpt-v-3 about 90% of the time
+        # call get_available_deployment 1k times, it should pick azure/gpt-4.1-nano about 90% of the time
         for _ in range(1000):
             selected_model = router.get_available_deployment("claude-1")
             selected_model_id = selected_model["litellm_params"]["model"]
@@ -296,7 +296,7 @@ def test_weighted_selection_router_no_rpm_set():
 
         total_requests = sum(selection_counts.values())
 
-        # Assert that 'azure/chatgpt-v-3' has about 90% of the total requests
+        # Assert that 'azure/gpt-4.1-nano' has about 90% of the total requests
         assert (
             selection_counts["bedrock/claude1.2"] / total_requests == 1
         ), f"Assertion failed: Selection counts {selection_counts}"
@@ -325,7 +325,7 @@ def test_model_group_aliases():
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_base": os.getenv("AZURE_API_BASE"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
@@ -358,7 +358,7 @@ def test_model_group_aliases():
                 )
 
         # test that
-        # call get_available_deployment 1k times, it should pick azure/chatgpt-v-3 about 90% of the time
+        # call get_available_deployment 1k times, it should pick azure/gpt-4.1-nano about 90% of the time
         selection_counts = defaultdict(int)
         for _ in range(1000):
             selected_model = router.get_available_deployment("gpt-3.5-turbo")
@@ -369,10 +369,10 @@ def test_model_group_aliases():
 
         total_requests = sum(selection_counts.values())
 
-        # Assert that 'azure/chatgpt-v-3' has about 90% of the total requests
+        # Assert that 'azure/gpt-4.1-nano' has about 90% of the total requests
         assert (
-            selection_counts["azure/chatgpt-v-3"] / total_requests > 0.89
-        ), f"Assertion failed: 'azure/chatgpt-v-3' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}"
+            selection_counts["azure/gpt-4.1-nano"] / total_requests > 0.89
+        ), f"Assertion failed: 'azure/gpt-4.1-nano' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}"
 
         router.reset()
     except Exception as e:
@@ -553,7 +553,7 @@ async def test_weighted_selection_router_async(rpm_list, tpm_list):
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_base": os.getenv("AZURE_API_BASE"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
@@ -567,7 +567,7 @@ async def test_weighted_selection_router_async(rpm_list, tpm_list):
         )
         selection_counts = defaultdict(int)
 
-        # call get_available_deployment 1k times, it should pick azure/chatgpt-v-3 about 90% of the time
+        # call get_available_deployment 1k times, it should pick azure/gpt-4.1-nano about 90% of the time
         for _ in range(1000):
             selected_model = await router.async_get_available_deployment(
                 "gpt-3.5-turbo", request_kwargs={}
@@ -580,13 +580,13 @@ async def test_weighted_selection_router_async(rpm_list, tpm_list):
         total_requests = sum(selection_counts.values())
 
         if rpm_list[0] is not None or tpm_list[0] is not None:
-            # Assert that 'azure/chatgpt-v-3' has about 90% of the total requests
+            # Assert that 'azure/gpt-4.1-nano' has about 90% of the total requests
             assert (
-                selection_counts["azure/chatgpt-v-3"] / total_requests > 0.89
-            ), f"Assertion failed: 'azure/chatgpt-v-3' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}"
+                selection_counts["azure/gpt-4.1-nano"] / total_requests > 0.89
+            ), f"Assertion failed: 'azure/gpt-4.1-nano' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}"
         else:
             # Assert both are used
-            assert selection_counts["azure/chatgpt-v-3"] > 0
+            assert selection_counts["azure/gpt-4.1-nano"] > 0
             assert selection_counts["gpt-3.5-turbo"] > 0
         router.reset()
     except Exception as e:
diff --git a/tests/local_testing/test_router_init.py b/tests/local_testing/test_router_init.py
index dd2d43dc26..1c7ab45908 100644
--- a/tests/local_testing/test_router_init.py
+++ b/tests/local_testing/test_router_init.py
@@ -40,7 +40,7 @@
 #             {
 #                 "model_name": "gpt-3.5-turbo",
 #                 "litellm_params": {
-#                     "model": "azure/chatgpt-v-3",
+#                     "model": "azure/gpt-4.1-nano",
 #                     "api_key": os.getenv("AZURE_API_KEY"),
 #                     "api_version": os.getenv("AZURE_API_VERSION"),
 #                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -96,7 +96,7 @@
 #             {
 #                 "model_name": "gpt-3.5-turbo",
 #                 "litellm_params": {
-#                     "model": "azure/chatgpt-v-3",
+#                     "model": "azure/gpt-4.1-nano",
 #                     "api_key": os.getenv("AZURE_API_KEY"),
 #                     "api_version": os.getenv("AZURE_API_VERSION"),
 #                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -134,7 +134,7 @@
 #             {
 #                 "model_name": "azure-cloudflare",
 #                 "litellm_params": {
-#                     "model": "azure/chatgpt-v-3",
+#                     "model": "azure/gpt-4.1-nano",
 #                     "api_key": os.getenv("AZURE_API_KEY"),
 #                     "api_version": os.getenv("AZURE_API_VERSION"),
 #                     "api_base": "https://gateway.ai.cloudflare.com/v1/0399b10e77ac6668c80404a5ff49eb37/litellm-test/azure-openai/openai-gpt-4-test-v-1",
@@ -201,7 +201,7 @@
 #             {
 #                 "model_name": "gpt-3.5-turbo",
 #                 "litellm_params": {
-#                     "model": "azure/chatgpt-v-3",
+#                     "model": "azure/gpt-4.1-nano",
 #                     "api_key": os.getenv("AZURE_API_KEY"),
 #                     "api_version": os.getenv("AZURE_API_VERSION"),
 #                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -254,7 +254,7 @@
 #             {
 #                 "model_name": "gpt-3.5-turbo",
 #                 "litellm_params": {
-#                     "model": "azure/chatgpt-v-3",
+#                     "model": "azure/gpt-4.1-nano",
 #                     "api_key": os.getenv("AZURE_API_KEY"),
 #                     "api_version": os.getenv("AZURE_API_VERSION"),
 #                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -615,7 +615,7 @@
 #             {
 #                 "model_name": "gpt-3.5-turbo",
 #                 "litellm_params": {
-#                     "model": "azure/chatgpt-v-3",
+#                     "model": "azure/gpt-4.1-nano",
 #                     "api_key": os.getenv("AZURE_API_KEY"),
 #                     "api_version": os.getenv("AZURE_API_VERSION"),
 #                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -660,7 +660,7 @@
 #         {
 #             "model_name": "gpt-3.5-turbo",
 #             "litellm_params": {
-#                 "model": "azure/chatgpt-v-3",
+#                 "model": "azure/gpt-4.1-nano",
 #                 "api_key": os.getenv("AZURE_API_KEY"),
 #                 "api_version": os.getenv("AZURE_API_VERSION"),
 #                 "api_base": os.getenv("AZURE_API_BASE"),
diff --git a/tests/local_testing/test_router_retries.py b/tests/local_testing/test_router_retries.py
index d028010afa..9246ea1801 100644
--- a/tests/local_testing/test_router_retries.py
+++ b/tests/local_testing/test_router_retries.py
@@ -166,7 +166,7 @@ async def test_router_retry_policy(error_type):
             {
                 "model_name": "gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -175,7 +175,7 @@ async def test_router_retry_policy(error_type):
             {
                 "model_name": "bad-model",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": "bad-key",
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -275,7 +275,7 @@ async def test_dynamic_router_retry_policy(model_group):
             {
                 "model_name": "gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -287,7 +287,7 @@ async def test_dynamic_router_retry_policy(model_group):
             {
                 "model_name": "gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -299,7 +299,7 @@ async def test_dynamic_router_retry_policy(model_group):
             {
                 "model_name": "gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -311,7 +311,7 @@ async def test_dynamic_router_retry_policy(model_group):
             {
                 "model_name": "bad-model",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": "bad-key",
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -393,7 +393,7 @@ def test_retry_rate_limit_error_with_healthy_deployments():
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -426,7 +426,7 @@ def test_do_retry_rate_limit_error_with_no_fallbacks_and_no_healthy_deployments(
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -459,14 +459,14 @@ def test_raise_context_window_exceeded_error():
         llm_provider="azure",
         model="gpt-3.5-turbo",
     )
-    context_window_fallbacks = [{"gpt-3.5-turbo": ["azure/chatgpt-v-3"]}]
+    context_window_fallbacks = [{"gpt-3.5-turbo": ["azure/gpt-4.1-nano"]}]
 
     router = Router(
         model_list=[
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -508,7 +508,7 @@ def test_raise_context_window_exceeded_error_no_retry():
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -562,7 +562,7 @@ def test_timeout_for_rate_limit_error_with_healthy_deployments(
         {
             "model_name": "gpt-3.5-turbo",
             "litellm_params": {
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_key": os.getenv("AZURE_API_KEY"),
                 "api_version": os.getenv("AZURE_API_VERSION"),
                 "api_base": os.getenv("AZURE_API_BASE"),
@@ -589,7 +589,7 @@ def test_timeout_for_rate_limit_error_with_healthy_deployments(
                 "litellm_params": {
                     "api_key": "my-key",
                     "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com",
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                 },
                 "model_info": {
                     "id": "0e30bc8a63fa91ae4415d4234e231b3f9e6dd900cac57d118ce13a720d95e9d6",
@@ -615,7 +615,7 @@ def test_timeout_for_rate_limit_error_with_no_healthy_deployments():
         {
             "model_name": "gpt-3.5-turbo",
             "litellm_params": {
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_key": os.getenv("AZURE_API_KEY"),
                 "api_version": os.getenv("AZURE_API_VERSION"),
                 "api_base": os.getenv("AZURE_API_BASE"),
@@ -650,7 +650,7 @@ def test_no_retry_for_not_found_error_404():
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -709,7 +709,7 @@ def test_no_retry_when_no_healthy_deployments():
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
diff --git a/tests/local_testing/test_router_timeout.py b/tests/local_testing/test_router_timeout.py
index 882c168435..7a04db1b3b 100644
--- a/tests/local_testing/test_router_timeout.py
+++ b/tests/local_testing/test_router_timeout.py
@@ -30,7 +30,7 @@ def test_router_timeouts():
         {
             "model_name": "openai-gpt-4",
             "litellm_params": {
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_key": "os.environ/AZURE_API_KEY",
                 "api_base": "os.environ/AZURE_API_BASE",
                 "api_version": "os.environ/AZURE_API_VERSION",
diff --git a/tests/local_testing/test_router_utils.py b/tests/local_testing/test_router_utils.py
index e5ead7a701..e27588babc 100644
--- a/tests/local_testing/test_router_utils.py
+++ b/tests/local_testing/test_router_utils.py
@@ -32,7 +32,7 @@ def test_returned_settings():
             {
                 "model_name": "gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": "bad-key",
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -96,7 +96,7 @@ def test_update_kwargs_before_fallbacks_unit_test():
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": "bad-key",
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -133,7 +133,7 @@ async def test_update_kwargs_before_fallbacks(call_type):
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": "bad-key",
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
diff --git a/tests/local_testing/test_streaming.py b/tests/local_testing/test_streaming.py
index 630ae2b184..0520c464df 100644
--- a/tests/local_testing/test_streaming.py
+++ b/tests/local_testing/test_streaming.py
@@ -243,7 +243,7 @@ tools_schema = [
 def test_completion_azure_stream_special_char():
     litellm.set_verbose = True
     messages = [{"role": "user", "content": "hi. respond with the <xml> tag only"}]
-    response = completion(model="azure/chatgpt-v-3", messages=messages, stream=True)
+    response = completion(model="azure/gpt-4.1-nano", messages=messages, stream=True)
     response_str = ""
     for part in response:
         response_str += part.choices[0].delta.content or ""
@@ -451,7 +451,7 @@ def test_completion_azure_stream():
             },
         ]
         response = completion(
-            model="azure/chatgpt-v-3", messages=messages, stream=True, max_tokens=50
+            model="azure/gpt-4.1-nano", messages=messages, stream=True, max_tokens=50
         )
         complete_response = ""
         # Add any assertions here to check the response
@@ -589,7 +589,7 @@ def test_completion_azure_function_calling_stream():
         user_message = "What is the current weather in Boston?"
         messages = [{"content": user_message, "role": "user"}]
         response = completion(
-            model="azure/chatgpt-functioncalling",
+            model="azure/gpt-4.1-nano",
             messages=messages,
             stream=True,
             tools=tools_schema,
@@ -1101,7 +1101,7 @@ def test_vertex_ai_stream(provider):
     litellm.vertex_project = "pathrise-convert-1606954137718"
     import random
 
-    test_models = ["gemini-1.5-pro"]
+    test_models = ["gemini-2.5-flash-lite"]
     for model in test_models:
         try:
             print("making request", model)
@@ -1952,10 +1952,9 @@ def test_openai_chat_completion_complete_response_call():
     "model",
     [
         "gpt-3.5-turbo",
-        "azure/chatgpt-v-3",
+        "azure/gpt-4.1-nano",
         "claude-3-haiku-20240307",
         "o1",
-        "azure/fake-o1-mini",
     ],
 )
 @pytest.mark.parametrize(
diff --git a/tests/local_testing/test_timeout.py b/tests/local_testing/test_timeout.py
index 007c32e447..92f6435ea9 100644
--- a/tests/local_testing/test_timeout.py
+++ b/tests/local_testing/test_timeout.py
@@ -22,7 +22,7 @@ import litellm
     "model, provider",
     [
         ("gpt-3.5-turbo", "openai"),
-        ("azure/chatgpt-v-3", "azure"),
+        ("azure/gpt-4.1-nano", "azure"),
     ],
 )
 @pytest.mark.parametrize("sync_mode", [True, False])
@@ -157,7 +157,7 @@ def test_hanging_request_openai():
                 {
                     "model_name": "azure-gpt",
                     "litellm_params": {
-                        "model": "azure/chatgpt-v-3",
+                        "model": "azure/gpt-4.1-nano",
                         "api_base": os.environ["AZURE_API_BASE"],
                         "api_key": os.environ["AZURE_API_KEY"],
                     },
diff --git a/tests/local_testing/test_tpm_rpm_routing_v2.py b/tests/local_testing/test_tpm_rpm_routing_v2.py
index 6464362c7c..92d2d59785 100644
--- a/tests/local_testing/test_tpm_rpm_routing_v2.py
+++ b/tests/local_testing/test_tpm_rpm_routing_v2.py
@@ -45,7 +45,7 @@ def test_tpm_rpm_updated():
     )
     model_group = "gpt-3.5-turbo"
     deployment_id = "1234"
-    deployment = "azure/chatgpt-v-3"
+    deployment = "azure/gpt-4.1-nano"
     total_tokens = 50
     standard_logging_payload: StandardLoggingPayload = create_standard_logging_payload()
     standard_logging_payload["model_group"] = model_group
@@ -100,12 +100,12 @@ def test_get_available_deployments():
     model_list = [
         {
             "model_name": "gpt-3.5-turbo",
-            "litellm_params": {"model": "azure/chatgpt-v-3"},
+            "litellm_params": {"model": "azure/gpt-4.1-nano"},
             "model_info": {"id": "1234"},
         },
         {
             "model_name": "gpt-3.5-turbo",
-            "litellm_params": {"model": "azure/chatgpt-v-3"},
+            "litellm_params": {"model": "azure/gpt-4.1-nano"},
             "model_info": {"id": "5678"},
         },
     ]
@@ -116,7 +116,7 @@ def test_get_available_deployments():
     ## DEPLOYMENT 1 ##
     total_tokens = 50
     deployment_id = "1234"
-    deployment = "azure/chatgpt-v-3"
+    deployment = "azure/gpt-4.1-nano"
     standard_logging_payload = create_standard_logging_payload()
     standard_logging_payload["model_group"] = model_group
     standard_logging_payload["model_id"] = deployment_id
@@ -733,7 +733,7 @@ async def test_tpm_rpm_routing_model_name_checks():
     deployment = {
         "model_name": "gpt-3.5-turbo",
         "litellm_params": {
-            "model": "azure/chatgpt-v-3",
+            "model": "azure/gpt-4.1-nano",
             "api_key": os.getenv("AZURE_API_KEY"),
             "api_base": os.getenv("AZURE_API_BASE"),
             "mock_response": "Hey, how's it going?",
@@ -775,5 +775,5 @@ async def test_tpm_rpm_routing_model_name_checks():
 
         assert (
             standard_logging_payload["hidden_params"]["litellm_model_name"]
-            == "azure/chatgpt-v-3"
+            == "azure/gpt-4.1-nano"
         )
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/completion.json b/tests/logging_callback_tests/langfuse_expected_request_body/completion.json
index b995df2d44..74106b19b3 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/completion.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/completion.json
@@ -36,10 +36,10 @@
                         "model_id": null,
                         "cache_key": null,
                         "api_base": "https://api.openai.com",
-                        "response_cost": 5.4999999999999995e-05,
+                        "response_cost": 3.5e-05,
                         "additional_headers": {}
                     },
-                    "litellm_response_cost": 5.4999999999999995e-05,
+                    "litellm_response_cost": 3.5e-05,
                     "cache_hit": false,
                     "requester_metadata": {}
                 },
@@ -67,7 +67,7 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
                         "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_complex_metadata.json b/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_complex_metadata.json
index 348fe5956d..5a5a32de2e 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_complex_metadata.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_complex_metadata.json
@@ -108,7 +108,7 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_langfuse_metadata.json b/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_langfuse_metadata.json
index b63bedf16a..1da758d1db 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_langfuse_metadata.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_langfuse_metadata.json
@@ -52,11 +52,11 @@
                         "model_id": null,
                         "cache_key": null,
                         "api_base": "https://api.openai.com",
-                        "response_cost": 5.4999999999999995e-05,
+                        "response_cost": 3.5e-05,
                         "additional_headers": {},
                         "litellm_overhead_time_ms": null
                     },
-                    "litellm_response_cost": 5.4999999999999995e-05,
+                    "litellm_response_cost": 3.5e-05,
                     "cache_hit": false,
                     "requester_metadata": {}
                 },
@@ -86,13 +86,13 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
-                    "cache_creation_input_tokens": 0,
-                    "cache_read_input_tokens": 0,
                     "input": 10,
-                    "output": 20
+                    "output": 20,
+                    "cache_creation_input_tokens": 0,
+                    "cache_read_input_tokens": 0
                 }
             },
             "timestamp": "2025-01-22T17:19:11.235541Z"
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_no_choices.json b/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_no_choices.json
index f4242d7edc..b01c73ffd8 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_no_choices.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_no_choices.json
@@ -57,7 +57,7 @@
                     "input": 10,
                     "output": 10,
                     "unit": "TOKENS",
-                    "totalCost": 3.5e-05
+                    "totalCost": 1.9999999999999998e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_router.json b/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_router.json
index 84ea9768f0..feb7aff80b 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_router.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_router.json
@@ -72,7 +72,7 @@
                     "input": 10,
                     "output": 10,
                     "unit": "TOKENS",
-                    "totalCost": 3.5e-05
+                    "totalCost": 1.9999999999999998e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_tags.json b/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_tags.json
index e7442ce0a0..d4a3f4a57a 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_tags.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_tags.json
@@ -76,7 +76,7 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_tags_stream.json b/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_tags_stream.json
index 04c24c8963..d31ea5eb3c 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_tags_stream.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/completion_with_tags_stream.json
@@ -76,7 +76,7 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/complex_metadata.json b/tests/logging_callback_tests/langfuse_expected_request_body/complex_metadata.json
index a21ab058fc..b9e8286dbf 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/complex_metadata.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/complex_metadata.json
@@ -83,7 +83,7 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/complex_metadata_2.json b/tests/logging_callback_tests/langfuse_expected_request_body/complex_metadata_2.json
index 3f400f6591..78358b021a 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/complex_metadata_2.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/complex_metadata_2.json
@@ -75,7 +75,7 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/empty_metadata.json b/tests/logging_callback_tests/langfuse_expected_request_body/empty_metadata.json
index 1873d4a6ea..057278c001 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/empty_metadata.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/empty_metadata.json
@@ -69,7 +69,7 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/metadata_with_function.json b/tests/logging_callback_tests/langfuse_expected_request_body/metadata_with_function.json
index 34e3c9f8da..5313428135 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/metadata_with_function.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/metadata_with_function.json
@@ -69,7 +69,7 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/metadata_with_lock.json b/tests/logging_callback_tests/langfuse_expected_request_body/metadata_with_lock.json
index 1873d4a6ea..057278c001 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/metadata_with_lock.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/metadata_with_lock.json
@@ -69,7 +69,7 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/nested_metadata.json b/tests/logging_callback_tests/langfuse_expected_request_body/nested_metadata.json
index 4d6ce12ec6..a4dc890273 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/nested_metadata.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/nested_metadata.json
@@ -75,7 +75,7 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/simple_metadata.json b/tests/logging_callback_tests/langfuse_expected_request_body/simple_metadata.json
index 5e5edc795e..616b64e1bc 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/simple_metadata.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/simple_metadata.json
@@ -75,7 +75,7 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/simple_metadata2.json b/tests/logging_callback_tests/langfuse_expected_request_body/simple_metadata2.json
index 7bbf4e4eee..9a7b5833a8 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/simple_metadata2.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/simple_metadata2.json
@@ -79,7 +79,7 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/langfuse_expected_request_body/simple_metadata3.json b/tests/logging_callback_tests/langfuse_expected_request_body/simple_metadata3.json
index fcdcb47aea..c8addd47da 100644
--- a/tests/logging_callback_tests/langfuse_expected_request_body/simple_metadata3.json
+++ b/tests/logging_callback_tests/langfuse_expected_request_body/simple_metadata3.json
@@ -83,7 +83,7 @@
                     "input": 10,
                     "output": 20,
                     "unit": "TOKENS",
-                    "totalCost": 5.4999999999999995e-05
+                    "totalCost": 3.5e-05
                 },
                 "usageDetails": {
                     "cache_creation_input_tokens": 0,
diff --git a/tests/logging_callback_tests/test_amazing_s3_logs.py b/tests/logging_callback_tests/test_amazing_s3_logs.py
index c9d0987e86..c471f23b95 100644
--- a/tests/logging_callback_tests/test_amazing_s3_logs.py
+++ b/tests/logging_callback_tests/test_amazing_s3_logs.py
@@ -384,7 +384,7 @@ async def make_async_calls():
     for _ in range(5):
         task = asyncio.create_task(
             litellm.acompletion(
-                model="azure/chatgpt-v-3",
+                model="azure/gpt-4.1-nano",
                 messages=[{"role": "user", "content": "This is a test"}],
                 max_tokens=5,
                 temperature=0.7,
diff --git a/tests/logging_callback_tests/test_custom_callback_router.py b/tests/logging_callback_tests/test_custom_callback_router.py
index 1e3f5f4cfb..f7c0c436ec 100644
--- a/tests/logging_callback_tests/test_custom_callback_router.py
+++ b/tests/logging_callback_tests/test_custom_callback_router.py
@@ -503,7 +503,7 @@ async def test_async_embedding_azure():
             {
                 "model_name": "azure-embedding-model",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/azure-embedding-model",
+                    "model": "azure/text-embedding-ada-002",
                     "api_key": "my-bad-key",
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
@@ -546,7 +546,7 @@ async def test_async_chat_azure_with_fallbacks():
             {
                 "model_name": "gpt-3.5-turbo",  # openai model name
                 "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": "my-bad-key",
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
diff --git a/tests/logging_callback_tests/test_spend_logs.py b/tests/logging_callback_tests/test_spend_logs.py
index 500419eb04..6b1ef91673 100644
--- a/tests/logging_callback_tests/test_spend_logs.py
+++ b/tests/logging_callback_tests/test_spend_logs.py
@@ -89,7 +89,7 @@ def test_spend_logs_payload(model_id: Optional[str]):
                     },
                     "endpoint": "http://localhost:4000/chat/completions",
                     "model_group": "gpt-3.5-turbo",
-                    "deployment": "azure/chatgpt-v-3",
+                    "deployment": "azure/gpt-4.1-nano",
                     "model_info": {
                         "id": "4bad40a1eb6bebd1682800f16f44b9f06c52a6703444c99c7f9f32e9de3693b4",
                         "db_model": False,
diff --git a/tests/old_proxy_tests/tests/load_test_q.py b/tests/old_proxy_tests/tests/load_test_q.py
index a0e22eda5a..a50b0fbcab 100644
--- a/tests/old_proxy_tests/tests/load_test_q.py
+++ b/tests/old_proxy_tests/tests/load_test_q.py
@@ -25,7 +25,7 @@ config = {
         {
             "model_name": "gpt-3.5-turbo",
             "litellm_params": {
-                "model": "azure/chatgpt-v-3",
+                "model": "azure/gpt-4.1-nano",
                 "api_key": os.environ["AZURE_API_KEY"],
                 "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com/",
                 "api_version": "2023-07-01-preview",
diff --git a/tests/old_proxy_tests/tests/test_langchain_request.py b/tests/old_proxy_tests/tests/test_langchain_request.py
index 901edd783a..0f7c3b3af2 100644
--- a/tests/old_proxy_tests/tests/test_langchain_request.py
+++ b/tests/old_proxy_tests/tests/test_langchain_request.py
@@ -9,7 +9,7 @@
 
 # chat = ChatOpenAI(
 #     openai_api_base="http://0.0.0.0:8000",
-#     model = "azure/chatgpt-v-3",
+#     model = "azure/gpt-4.1-nano",
 #     temperature=0.1,
 #     extra_body={
 #         "metadata": {
diff --git a/tests/old_proxy_tests/tests/test_openai_request.py b/tests/old_proxy_tests/tests/test_openai_request.py
index 41b8c43f2d..21d2a01be6 100644
--- a/tests/old_proxy_tests/tests/test_openai_request.py
+++ b/tests/old_proxy_tests/tests/test_openai_request.py
@@ -4,7 +4,7 @@ client = openai.OpenAI(api_key="hi", base_url="http://0.0.0.0:8000")
 
 # # request sent to model set on litellm proxy, `litellm --model`
 response = client.chat.completions.create(
-    model="azure/chatgpt-v-3",
+    model="azure/gpt-4.1-nano",
     messages=[
         {"role": "user", "content": "this is a test request, write a short poem"}
     ],
diff --git a/tests/pass_through_tests/test_gemini_with_spend.test.js b/tests/pass_through_tests/test_gemini_with_spend.test.js
index 84010ecee1..7a6584d161 100644
--- a/tests/pass_through_tests/test_gemini_with_spend.test.js
+++ b/tests/pass_through_tests/test_gemini_with_spend.test.js
@@ -29,7 +29,7 @@ describe('Gemini AI Tests', () => {
         };
 
         const model = genAI.getGenerativeModel({
-            model: 'gemini-1.5-pro'
+            model: 'gemini-2.5-flash-lite'
         }, requestOptions);
 
         const prompt = 'Say "hello test" and nothing else';
@@ -77,7 +77,7 @@ describe('Gemini AI Tests', () => {
         };
 
         const model = genAI.getGenerativeModel({
-            model: 'gemini-1.5-pro'
+            model: 'gemini-2.5-flash-lite'
         }, requestOptions);
 
         const prompt = 'Say "hello test" and nothing else';
diff --git a/tests/pass_through_tests/test_local_gemini.js b/tests/pass_through_tests/test_local_gemini.js
index 1f3f7f8a0d..0a72ca5cd7 100644
--- a/tests/pass_through_tests/test_local_gemini.js
+++ b/tests/pass_through_tests/test_local_gemini.js
@@ -1,13 +1,13 @@
 const { GoogleGenerativeAI, ModelParams, RequestOptions } = require("@google/generative-ai");
 
 const modelParams = {
-    model: 'gemini-1.5-pro',
+    model: 'gemini-2.5-flash-lite',
 };
   
 const requestOptions = {
     baseUrl: 'http://127.0.0.1:4000/gemini',
     customHeaders: {
-        "tags": "gemini-js-sdk,gemini-1.5-pro"
+        "tags": "gemini-js-sdk,gemini-2.5-flash-lite"
     }
 };
   
diff --git a/tests/pass_through_tests/test_local_vertex.js b/tests/pass_through_tests/test_local_vertex.js
index a94c6746e9..231858cdf9 100644
--- a/tests/pass_through_tests/test_local_vertex.js
+++ b/tests/pass_through_tests/test_local_vertex.js
@@ -20,7 +20,7 @@ const requestOptions = {
 };
 
 const generativeModel = vertexAI.getGenerativeModel(
-    { model: 'gemini-1.5-pro' },
+    { model: 'gemini-2.5-flash-lite' },
     requestOptions
 );
 
diff --git a/tests/pass_through_tests/test_vertex.test.js b/tests/pass_through_tests/test_vertex.test.js
index dccd649402..c10889e3a6 100644
--- a/tests/pass_through_tests/test_vertex.test.js
+++ b/tests/pass_through_tests/test_vertex.test.js
@@ -75,7 +75,7 @@ describe('Vertex AI Tests', () => {
         };
 
         const generativeModel = vertexAI.getGenerativeModel(
-            { model: 'gemini-1.5-pro' },
+            { model: 'gemini-2.5-flash-lite' },
             requestOptions
         );
 
@@ -103,7 +103,7 @@ describe('Vertex AI Tests', () => {
         const vertexAI = new VertexAI({project: 'pathrise-convert-1606954137718', location: 'us-central1', apiEndpoint: "localhost:4000/vertex-ai"});
         const customHeaders = new Headers({"x-litellm-api-key": "sk-1234"});
         const requestOptions = {customHeaders: customHeaders};
-        const generativeModel = vertexAI.getGenerativeModel({model: 'gemini-1.5-pro'}, requestOptions);
+        const generativeModel = vertexAI.getGenerativeModel({model: 'gemini-2.5-flash-lite'}, requestOptions);
         const request = {contents: [{role: 'user', parts: [{text: 'What is 2+2?'}]}]};
 
         const result = await generativeModel.generateContent(request);
diff --git a/tests/pass_through_tests/test_vertex_ai.py b/tests/pass_through_tests/test_vertex_ai.py
index 4c32112296..dbcf93ee55 100644
--- a/tests/pass_through_tests/test_vertex_ai.py
+++ b/tests/pass_through_tests/test_vertex_ai.py
@@ -104,7 +104,7 @@ async def test_basic_vertex_ai_pass_through_with_spendlog():
         api_transport="rest",
     )
 
-    model = GenerativeModel(model_name="gemini-1.5-pro")
+    model = GenerativeModel(model_name="gemini-2.5-flash-lite")
     response = model.generate_content("hi")
 
     print("response", response)
@@ -136,7 +136,7 @@ async def test_basic_vertex_ai_pass_through_streaming_with_spendlog():
         api_transport="rest",
     )
 
-    model = GenerativeModel(model_name="gemini-1.5-pro")
+    model = GenerativeModel(model_name="gemini-2.5-flash-lite")
     response = model.generate_content("hi", stream=True)
 
     for chunk in response:
@@ -192,7 +192,7 @@ async def test_vertex_ai_pass_through_endpoint_context_caching():
     ]
 
     cached_content = caching.CachedContent.create(
-        model_name="gemini-1.5-pro-001",
+        model_name="gemini-2.5-flash-lite-001",
         system_instruction=system_instruction,
         contents=contents,
         ttl=datetime.timedelta(minutes=60),
diff --git a/tests/pass_through_tests/test_vertex_with_spend.test.js b/tests/pass_through_tests/test_vertex_with_spend.test.js
index 9d10ab4a25..acfeffd6b9 100644
--- a/tests/pass_through_tests/test_vertex_with_spend.test.js
+++ b/tests/pass_through_tests/test_vertex_with_spend.test.js
@@ -85,7 +85,7 @@ describe('Vertex AI Tests', () => {
         };
 
         const generativeModel = vertexAI.getGenerativeModel(
-            { model: 'gemini-1.5-pro' },
+            { model: 'gemini-2.5-flash-lite' },
             requestOptions
         );
 
@@ -142,7 +142,7 @@ describe('Vertex AI Tests', () => {
         };
 
         const generativeModel = vertexAI.getGenerativeModel(
-            { model: 'gemini-1.5-pro' },
+            { model: 'gemini-2.5-flash-lite' },
             requestOptions
         );
 
diff --git a/tests/proxy_unit_tests/example_config_yaml/azure_config.yaml b/tests/proxy_unit_tests/example_config_yaml/azure_config.yaml
index 111813c884..a0300bd6d1 100644
--- a/tests/proxy_unit_tests/example_config_yaml/azure_config.yaml
+++ b/tests/proxy_unit_tests/example_config_yaml/azure_config.yaml
@@ -1,7 +1,7 @@
 model_list:
   - model_name: gpt-4-team1
     litellm_params:
-      model: azure/chatgpt-v-3
+      model: azure/gpt-4.1-nano
       api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
       api_version: "2023-05-15"
       api_key:  os.environ/AZURE_API_KEY
diff --git a/tests/proxy_unit_tests/test_configs/custom_callbacks.py b/tests/proxy_unit_tests/test_configs/custom_callbacks.py
index 42f88b5d19..c7d66c068e 100644
--- a/tests/proxy_unit_tests/test_configs/custom_callbacks.py
+++ b/tests/proxy_unit_tests/test_configs/custom_callbacks.py
@@ -61,7 +61,7 @@ class testCustomCallbackProxy(CustomLogger):
         print("Value of async success: ", self.async_success)
         print("\n kwargs: ", kwargs)
         if (
-            kwargs.get("model") == "azure-embedding-model"
+            kwargs.get("model") == "text-embedding-ada-002"
             or kwargs.get("model") == "ada"
         ):
             print("Got an embedding model", kwargs.get("model"))
@@ -118,4 +118,5 @@ class testCustomCallbackProxy(CustomLogger):
         self.async_completion_kwargs_fail = kwargs
 
 
+# Global instance that can be accessed from tests
 my_custom_logger = testCustomCallbackProxy()
diff --git a/tests/proxy_unit_tests/test_configs/test_bad_config.yaml b/tests/proxy_unit_tests/test_configs/test_bad_config.yaml
index 0a16ecb3c5..ae699fdecb 100644
--- a/tests/proxy_unit_tests/test_configs/test_bad_config.yaml
+++ b/tests/proxy_unit_tests/test_configs/test_bad_config.yaml
@@ -5,17 +5,17 @@ model_list:
       model: gpt-3.5-turbo
   - model_name: working-azure-gpt-3.5-turbo
     litellm_params:
-      model: azure/chatgpt-v-3
+      model: azure/gpt-4.1-nano
       api_base: os.environ/AZURE_API_BASE
       api_key: os.environ/AZURE_API_KEY
   - model_name: azure-gpt-3.5-turbo
     litellm_params:
-      model: azure/chatgpt-v-3
+      model: azure/gpt-4.1-nano
       api_base: os.environ/AZURE_API_BASE
       api_key: bad-key
   - model_name: azure-embedding
     litellm_params:
-      model: azure/azure-embedding-model
+      model: azure/text-embedding-ada-002
       api_base: os.environ/AZURE_API_BASE
       api_key: bad-key
     
\ No newline at end of file
diff --git a/tests/proxy_unit_tests/test_configs/test_cloudflare_azure_with_cache_config.yaml b/tests/proxy_unit_tests/test_configs/test_cloudflare_azure_with_cache_config.yaml
index aeadbeb872..acb0785850 100644
--- a/tests/proxy_unit_tests/test_configs/test_cloudflare_azure_with_cache_config.yaml
+++ b/tests/proxy_unit_tests/test_configs/test_cloudflare_azure_with_cache_config.yaml
@@ -1,7 +1,7 @@
 model_list:
   - model_name: azure-cloudflare
     litellm_params:
-      model: azure/chatgpt-v-3
+      model: azure/gpt-4.1-nano
       api_base: https://gateway.ai.cloudflare.com/v1/0399b10e77ac6668c80404a5ff49eb37/litellm-test/azure-openai/openai-gpt-4-test-v-1
       api_key: os.environ/AZURE_API_KEY
       api_version: 2023-07-01-preview
diff --git a/tests/proxy_unit_tests/test_configs/test_config_no_auth.yaml b/tests/proxy_unit_tests/test_configs/test_config_no_auth.yaml
index 075bf7a09d..1ddfee464f 100644
--- a/tests/proxy_unit_tests/test_configs/test_config_no_auth.yaml
+++ b/tests/proxy_unit_tests/test_configs/test_config_no_auth.yaml
@@ -12,7 +12,7 @@ model_list:
 - litellm_params:
     api_base: https://gateway.ai.cloudflare.com/v1/0399b10e77ac6668c80404a5ff49eb37/litellm-test/azure-openai/openai-gpt-4-test-v-1
     api_key: os.environ/AZURE_API_KEY
-    model: azure/chatgpt-v-3
+    model: azure/gpt-4.1-nano
   model_name: azure-cloudflare-model
 - litellm_params:
     api_base: https://openai-france-1234.openai.azure.com
@@ -52,7 +52,7 @@ model_list:
     api_base: os.environ/AZURE_API_BASE
     api_key: os.environ/AZURE_API_KEY
     api_version: 2023-07-01-preview
-    model: azure/azure-embedding-model
+    model: azure/text-embedding-ada-002
   model_info:
     mode: embedding
   model_name: azure-embedding-model
@@ -105,7 +105,7 @@ model_list:
     api_base: os.environ/AZURE_API_BASE
     api_key: os.environ/AZURE_API_KEY
     api_version: 2023-07-01-preview
-    model: azure/azure-embedding-model
+    model: azure/text-embedding-ada-002
   model_info:
     base_model: text-embedding-ada-002
     mode: embedding
diff --git a/tests/proxy_unit_tests/test_proxy_custom_logger.py b/tests/proxy_unit_tests/test_proxy_custom_logger.py
index 0fbe5a31c3..28de522a4c 100644
--- a/tests/proxy_unit_tests/test_proxy_custom_logger.py
+++ b/tests/proxy_unit_tests/test_proxy_custom_logger.py
@@ -80,7 +80,7 @@ def test_embedding(client):
             my_custom_logger.async_success_embedding is True
         )  # checks if the status of async_success is True, only the async_log_success_event can set this to true
         assert (
-            my_custom_logger.async_embedding_kwargs["model"] == "azure-embedding-model"
+            my_custom_logger.async_embedding_kwargs["model"] == "text-embedding-ada-002"
         )  # checks if kwargs passed to async_log_success_event are correct
         kwargs = my_custom_logger.async_embedding_kwargs
         litellm_params = kwargs.get("litellm_params")
diff --git a/tests/proxy_unit_tests/test_proxy_pass_user_config.py b/tests/proxy_unit_tests/test_proxy_pass_user_config.py
index 3ecc252264..7b05bddd3a 100644
--- a/tests/proxy_unit_tests/test_proxy_pass_user_config.py
+++ b/tests/proxy_unit_tests/test_proxy_pass_user_config.py
@@ -64,7 +64,7 @@ def test_chat_completion(client_no_auth):
             ModelConfig(
                 model_name="user-azure-instance",
                 litellm_params=CompletionRequest(
-                    model="azure/chatgpt-v-3",
+                    model="azure/gpt-4.1-nano",
                     api_key=os.getenv("AZURE_API_KEY"),
                     api_version=os.getenv("AZURE_API_VERSION"),
                     api_base=os.getenv("AZURE_API_BASE"),
diff --git a/tests/proxy_unit_tests/test_proxy_server.py b/tests/proxy_unit_tests/test_proxy_server.py
index 4e72580b8b..b5117a3a2a 100644
--- a/tests/proxy_unit_tests/test_proxy_server.py
+++ b/tests/proxy_unit_tests/test_proxy_server.py
@@ -452,7 +452,7 @@ def test_chat_completion_azure(mock_acompletion, client_no_auth):
     try:
         # Your test data
         test_data = {
-            "model": "azure/chatgpt-v-3",
+            "model": "azure/gpt-4.1-nano",
             "messages": [
                 {"role": "user", "content": "write 1 sentence poem"},
             ],
@@ -463,7 +463,7 @@ def test_chat_completion_azure(mock_acompletion, client_no_auth):
         response = client_no_auth.post("/v1/chat/completions", json=test_data)
 
         mock_acompletion.assert_called_once_with(
-            model="azure/chatgpt-v-3",
+            model="azure/gpt-4.1-nano",
             messages=[
                 {"role": "user", "content": "write 1 sentence poem"},
             ],
@@ -496,19 +496,19 @@ def test_openai_deployments_model_chat_completions_azure(
     try:
         # Your test data
         test_data = {
-            "model": "azure/chatgpt-v-3",
+            "model": "azure/gpt-4.1-nano",
             "messages": [
                 {"role": "user", "content": "write 1 sentence poem"},
             ],
             "max_tokens": 10,
         }
 
-        url = "/openai/deployments/azure/chatgpt-v-3/chat/completions"
+        url = "/openai/deployments/azure/gpt-4.1-nano/chat/completions"
         print(f"testing proxy server with Azure Request {url}")
         response = client_no_auth.post(url, json=test_data)
 
         mock_acompletion.assert_called_once_with(
-            model="azure/chatgpt-v-3",
+            model="azure/gpt-4.1-nano",
             messages=[
                 {"role": "user", "content": "write 1 sentence poem"},
             ],
@@ -541,14 +541,14 @@ def test_embedding(mock_aembedding, client_no_auth):
 
     try:
         test_data = {
-            "model": "azure/azure-embedding-model",
+            "model": "azure/text-embedding-ada-002",
             "input": ["good morning from litellm"],
         }
 
         response = client_no_auth.post("/v1/embeddings", json=test_data)
 
         mock_aembedding.assert_called_once_with(
-            model="azure/azure-embedding-model",
+            model="azure/text-embedding-ada-002",
             input=["good morning from litellm"],
             specific_deployment=True,
             metadata=mock.ANY,
@@ -1322,7 +1322,7 @@ async def test_add_callback_via_key(prisma_client):
     try:
         # Your test data
         test_data = {
-            "model": "azure/chatgpt-v-3",
+            "model": "azure/gpt-4.1-nano",
             "messages": [
                 {"role": "user", "content": "write 1 sentence poem"},
             ],
@@ -1416,7 +1416,7 @@ async def test_add_callback_via_key_litellm_pre_call_utils(
     request._url = URL(url="/chat/completions")
 
     test_data = {
-        "model": "azure/chatgpt-v-3",
+        "model": "azure/gpt-4.1-nano",
         "messages": [
             {"role": "user", "content": "write 1 sentence poem"},
         ],
@@ -1431,7 +1431,7 @@ async def test_add_callback_via_key_litellm_pre_call_utils(
 
     data = {
         "data": {
-            "model": "azure/chatgpt-v-3",
+            "model": "azure/gpt-4.1-nano",
             "messages": [{"role": "user", "content": "write 1 sentence poem"}],
             "max_tokens": 10,
             "mock_response": "Hello world",
@@ -1531,7 +1531,7 @@ async def test_disable_fallbacks_by_key(disable_fallbacks_set):
 
     key_metadata = {"disable_fallbacks": disable_fallbacks_set}
     existing_data = {
-        "model": "azure/chatgpt-v-3",
+        "model": "azure/gpt-4.1-nano",
         "messages": [{"role": "user", "content": "write 1 sentence poem"}],
     }
     data = LiteLLMProxyRequestSetup.add_key_level_controls(
@@ -1572,7 +1572,7 @@ async def test_add_callback_via_key_litellm_pre_call_utils_gcs_bucket(
     request._url = URL(url="/chat/completions")
 
     test_data = {
-        "model": "azure/chatgpt-v-3",
+        "model": "azure/gpt-4.1-nano",
         "messages": [
             {"role": "user", "content": "write 1 sentence poem"},
         ],
@@ -1587,7 +1587,7 @@ async def test_add_callback_via_key_litellm_pre_call_utils_gcs_bucket(
 
     data = {
         "data": {
-            "model": "azure/chatgpt-v-3",
+            "model": "azure/gpt-4.1-nano",
             "messages": [{"role": "user", "content": "write 1 sentence poem"}],
             "max_tokens": 10,
             "mock_response": "Hello world",
@@ -1705,7 +1705,7 @@ async def test_add_callback_via_key_litellm_pre_call_utils_langsmith(
     request._url = URL(url="/chat/completions")
 
     test_data = {
-        "model": "azure/chatgpt-v-3",
+        "model": "azure/gpt-4.1-nano",
         "messages": [
             {"role": "user", "content": "write 1 sentence poem"},
         ],
@@ -1720,7 +1720,7 @@ async def test_add_callback_via_key_litellm_pre_call_utils_langsmith(
 
     data = {
         "data": {
-            "model": "azure/chatgpt-v-3",
+            "model": "azure/gpt-4.1-nano",
             "messages": [{"role": "user", "content": "write 1 sentence poem"}],
             "max_tokens": 10,
             "mock_response": "Hello world",
@@ -1833,7 +1833,7 @@ async def test_gemini_pass_through_endpoint():
     scope = {
         "type": "http",
         "method": "POST",
-        "path": "/gemini/v1beta/models/gemini-1.5-flash:countTokens",
+        "path": "/gemini/v1beta/models/gemini-2.5-flash:countTokens",
         "query_string": b"key=sk-1234",
         "headers": [
             (b"content-type", b"application/json"),
@@ -1850,7 +1850,7 @@ async def test_gemini_pass_through_endpoint():
     )
 
     resp = await gemini_proxy_route(
-        endpoint="v1beta/models/gemini-1.5-flash:countTokens?key=sk-1234",
+        endpoint="v1beta/models/gemini-2.5-flash:countTokens?key=sk-1234",
         request=request,
         fastapi_response=Response(),
     )
diff --git a/tests/proxy_unit_tests/test_proxy_server_keys.py b/tests/proxy_unit_tests/test_proxy_server_keys.py
index 8b8e943ba7..77c1844839 100644
--- a/tests/proxy_unit_tests/test_proxy_server_keys.py
+++ b/tests/proxy_unit_tests/test_proxy_server_keys.py
@@ -171,7 +171,7 @@
 #         model_data = {
 #             "model_name": "azure-model",
 #             "litellm_params": {
-#                 "model": "azure/chatgpt-v-3",
+#                 "model": "azure/gpt-4.1-nano",
 #                 "api_key": os.getenv("AZURE_API_KEY"),
 #                 "api_base": os.getenv("AZURE_API_BASE"),
 #                 "api_version": os.getenv("AZURE_API_VERSION")
diff --git a/tests/router_unit_tests/test_router_index_management.py b/tests/router_unit_tests/test_router_index_management.py
new file mode 100644
index 0000000000..bdd52f66ba
--- /dev/null
+++ b/tests/router_unit_tests/test_router_index_management.py
@@ -0,0 +1,105 @@
+import sys
+import os
+import pytest
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+from litellm import Router
+
+
+class TestRouterIndexManagement:
+    """Test cases for router index management functions"""
+
+    @pytest.fixture
+    def router(self):
+        """Create a router instance for testing"""
+        return Router(model_list=[])
+
+    def test_update_deployment_indices_after_removal(self, router):
+        """Test _update_deployment_indices_after_removal function"""
+        # Setup: Add models to router with proper structure
+        router.model_list = [
+            {"model": "test1", "model_info": {"id": "model-1"}}, 
+            {"model": "test2", "model_info": {"id": "model-2"}}, 
+            {"model": "test3", "model_info": {"id": "model-3"}}
+        ]
+        router.model_id_to_deployment_index_map = {"model-1": 0, "model-2": 1, "model-3": 2}
+
+        # Test: Remove model-2 (index 1)
+        router._update_deployment_indices_after_removal(model_id="model-2", removal_idx=1)
+
+        # Verify: model-2 is removed from index
+        assert "model-2" not in router.model_id_to_deployment_index_map
+        # Verify: model-3 index is updated (2 -> 1)
+        assert router.model_id_to_deployment_index_map["model-3"] == 1
+        # Verify: model-1 index remains unchanged
+        assert router.model_id_to_deployment_index_map["model-1"] == 0
+
+    def test_build_model_id_to_deployment_index_map(self, router):
+        """Test _build_model_id_to_deployment_index_map function"""
+        model_list = [
+            {
+                "model_name": "gpt-3.5-turbo",
+                "litellm_params": {"model": "gpt-3.5-turbo"},
+                "model_info": {"id": "model-1"},
+            },
+            {
+                "model_name": "gpt-4",
+                "litellm_params": {"model": "gpt-4"},
+                "model_info": {"id": "model-2"},
+            },
+        ]
+
+        # Test: Build index from model list
+        router._build_model_id_to_deployment_index_map(model_list)
+
+        # Verify: model_list is populated
+        assert len(router.model_list) == 2
+        # Verify: model_id_to_deployment_index_map is correctly built
+        assert router.model_id_to_deployment_index_map["model-1"] == 0
+        assert router.model_id_to_deployment_index_map["model-2"] == 1
+
+    def test_add_model_to_list_and_index_map_from_model_info(self, router):
+        """Test _add_model_to_list_and_index_map extracting model_id from model_info"""
+        # Setup: Empty router
+        router.model_list = []
+        router.model_id_to_deployment_index_map = {}
+        
+        # Test: Add model without explicit model_id
+        model = {"model": "test-model", "model_info": {"id": "model-info-id"}}
+        router._add_model_to_list_and_index_map(model=model)
+        
+        # Verify: Model added to list
+        assert len(router.model_list) == 1
+        assert router.model_list[0] == model
+        
+        # Verify: Index map uses model_info.id
+        assert router.model_id_to_deployment_index_map["model-info-id"] == 0
+
+
+    def test_add_model_to_list_and_index_map_multiple_models(self, router):
+        """Test _add_model_to_list_and_index_map with multiple models to verify indexing"""
+        # Setup: Empty router
+        router.model_list = []
+        router.model_id_to_deployment_index_map = {}
+        
+        # Test: Add multiple models
+        model1 = {"model": "model1", "model_info": {"id": "id-1"}}
+        model2 = {"model": "model2", "model_info": {"id": "id-2"}}
+        model3 = {"model": "model3", "model_info": {"id": "id-3"}}
+        
+        router._add_model_to_list_and_index_map(model=model1, model_id="id-1")
+        router._add_model_to_list_and_index_map(model=model2, model_id="id-2")
+        router._add_model_to_list_and_index_map(model=model3, model_id="id-3")
+        
+        # Verify: All models added to list
+        assert len(router.model_list) == 3
+        assert router.model_list[0] == model1
+        assert router.model_list[1] == model2
+        assert router.model_list[2] == model3
+        
+        # Verify: Correct indices in map
+        assert router.model_id_to_deployment_index_map["id-1"] == 0
+        assert router.model_id_to_deployment_index_map["id-2"] == 1
+        assert router.model_id_to_deployment_index_map["id-3"] == 2
diff --git a/tests/test_litellm/llms/openai/test_openai_image_edit_transformation.py b/tests/test_litellm/llms/openai/test_openai_image_edit_transformation.py
index d0d378ddfd..cd5e297a77 100644
--- a/tests/test_litellm/llms/openai/test_openai_image_edit_transformation.py
+++ b/tests/test_litellm/llms/openai/test_openai_image_edit_transformation.py
@@ -1,6 +1,8 @@
 from io import BufferedReader, BytesIO
 from typing import Dict
+
 import pytest
+
 from litellm import image_edit
 from litellm.llms.openai.image_edit.transformation import OpenAIImageEditConfig
 from litellm.types.router import GenericLiteLLMParams
@@ -37,7 +39,7 @@ def test_transform_image_edit_request_basic(image_edit_config: OpenAIImageEditCo
 
     # Check that files contains the image
     assert len(files) == 1
-    assert files[0][0] == "image"  # field name
+    assert files[0][0] == "image[]"  # field name
     assert files[0][1][0] == "image.png"  # filename
     assert files[0][1][1] == image  # image data
     assert "image/png" in files[0][1][2]  # content type
@@ -73,7 +75,7 @@ def test_transform_image_edit_request_with_mask(image_edit_config: OpenAIImageEd
     assert len(files) == 2
     
     # Find image and mask in files
-    image_file = next(f for f in files if f[0] == "image")
+    image_file = next(f for f in files if f[0] == "image[]")
     mask_file = next(f for f in files if f[0] == "mask")
     
     assert image_file[1][0] == "image.png"
@@ -87,8 +89,8 @@ def test_transform_image_edit_request_with_mask(image_edit_config: OpenAIImageEd
 
 def test_transform_image_edit_request_with_buffered_reader(image_edit_config: OpenAIImageEditConfig):
     """Test transformation with BufferedReader as image input"""
-    import tempfile
     import os
+    import tempfile
     
     model = "dall-e-2"
     prompt = "Make the background blue"
@@ -122,7 +124,7 @@ def test_transform_image_edit_request_with_buffered_reader(image_edit_config: Op
 
             # Check that files contains the image with the original filename
             assert len(files) == 1
-            assert files[0][0] == "image"
+            assert files[0][0] == "image[]"
             # Should use the buffer's name (full path from the BufferedReader.name)
             assert files[0][1][0] == temp_file_path  # Uses full path from buffer.name
             assert files[0][1][1] == image_buffer  # Should be the buffer object
@@ -168,7 +170,7 @@ def test_transform_image_edit_request_with_optional_params(image_edit_config: Op
 
     # Check that files contains only the image
     assert len(files) == 1
-    assert files[0][0] == "image"
+    assert files[0][0] == "image[]"
     assert files[0][1][1] == image
 
 
@@ -204,8 +206,8 @@ def test_transform_image_edit_request_with_multiple_images(image_edit_config: Op
     # Check that files contains all three images and no mask
     assert len(files) == 3
     
-    # All files should be image entries
-    image_files = [f for f in files if f[0] == "image"]
+    # All files should be image entries with image[] key
+    image_files = [f for f in files if f[0] == "image[]"]
     assert len(image_files) == 3
     
     # Check that all image data is present
diff --git a/tests/test_litellm/log.txt b/tests/test_litellm/log.txt
index 8324baf549..6470b12fed 100644
--- a/tests/test_litellm/log.txt
+++ b/tests/test_litellm/log.txt
@@ -1,38 +1,2 @@
-============================= test session starts ==============================
-platform darwin -- Python 3.13.1, pytest-8.3.5, pluggy-1.5.0 -- /Users/krrishdholakia/Documents/litellm/myenv/bin/python3.13
-cachedir: .pytest_cache
-rootdir: /Users/krrishdholakia/Documents/litellm
-configfile: pyproject.toml
-plugins: respx-0.22.0, postgresql-7.0.1, anyio-4.4.0, asyncio-0.26.0, mock-3.14.0, ddtrace-2.19.0rc1, xdist-3.6.1
-asyncio: mode=Mode.STRICT, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function
-collecting ... collected 8 items
-
-test_main.py::test_url_with_format_param[False-anthropic/claude-3-5-sonnet] PASSED [ 12%]
-test_main.py::test_url_with_format_param[False-bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0] PASSED [ 25%]
-test_main.py::test_url_with_format_param[False-bedrock/invoke/anthropic.claude-3-5-sonnet-20240620-v1:0] PASSED [ 37%]
-test_main.py::test_url_with_format_param[False-gemini/gemini-1.5-flash] PASSED [ 50%]
-test_main.py::test_url_with_format_param[True-anthropic/claude-3-5-sonnet] PASSED [ 62%]
-test_main.py::test_url_with_format_param[True-bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0] PASSED [ 75%]
-test_main.py::test_url_with_format_param[True-bedrock/invoke/anthropic.claude-3-5-sonnet-20240620-v1:0] PASSED [ 87%]
-test_main.py::test_url_with_format_param[True-gemini/gemini-1.5-flash] PASSED [100%]
-
-=============================== warnings summary ===============================
-tests/litellm/test_main.py::test_url_with_format_param[False-bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0]
-tests/litellm/test_main.py::test_url_with_format_param[False-bedrock/invoke/anthropic.claude-3-5-sonnet-20240620-v1:0]
-tests/litellm/test_main.py::test_url_with_format_param[True-bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0]
-tests/litellm/test_main.py::test_url_with_format_param[True-bedrock/invoke/anthropic.claude-3-5-sonnet-20240620-v1:0]
-  /Users/krrishdholakia/Documents/litellm/myenv/lib/python3.13/site-packages/botocore/auth.py:425: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
-    datetime_now = datetime.datetime.utcnow()
-
-tests/litellm/test_main.py::test_url_with_format_param[True-anthropic/claude-3-5-sonnet]
-  /Users/krrishdholakia/Documents/litellm/myenv/lib/python3.13/site-packages/pydantic/main.py:421: UserWarning: Pydantic serializer warnings:
-    Expected `str` but got `MagicMock` with value `<MagicMock name='mock().j...em__()' id='5209845984'>` - serialized value may not be as expected
-    return self.__pydantic_serializer__.to_python(
-
-tests/litellm/test_main.py::test_url_with_format_param[True-bedrock/invoke/anthropic.claude-3-5-sonnet-20240620-v1:0]
-  /Users/krrishdholakia/Documents/litellm/myenv/lib/python3.13/site-packages/pydantic/main.py:421: UserWarning: Pydantic serializer warnings:
-    Expected `str` but got `MagicMock` with value `<MagicMock name='mock().j...em__()' id='5210168288'>` - serialized value may not be as expected
-    return self.__pydantic_serializer__.to_python(
-
--- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
-======================== 8 passed, 6 warnings in 2.33s =========================
+llms/bedrock/chat/invoke_agent/transformation.py:404: error: Incompatible types in assignment (expression has type "object", variable has type "InvokeAgentModelInvocationOutput | None")  [assignment]
+llms/bedrock/chat/invoke_agent/transformation.py:405: error: Argument 1 to "get" of "Mapping" has incompatible type "str | InvokeAgentModelInvocationOutput"; expected "str"  [typeddict-item]
diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_anthropic_auth_headers.py b/tests/test_litellm/proxy/pass_through_endpoints/test_anthropic_auth_headers.py
deleted file mode 100644
index 9872ed2c9d..0000000000
--- a/tests/test_litellm/proxy/pass_through_endpoints/test_anthropic_auth_headers.py
+++ /dev/null
@@ -1,218 +0,0 @@
-import os
-import sys
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-sys.path.insert(
-    0, os.path.abspath("../../..")
-)  # Adds the parent directory to the system path
-
-from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import (
-    anthropic_proxy_route,
-)
-
-
-class TestAnthropicAuthHeaders:
-    """Test authentication header handling in anthropic_proxy_route."""
-
-    @pytest.fixture
-    def mock_request(self):
-        """Create a mock request object."""
-        request = MagicMock()
-        request.method = "POST"
-        request.headers = {}
-        return request
-
-    @pytest.fixture
-    def mock_response(self):
-        """Create a mock FastAPI response object."""
-        return MagicMock()
-
-    @pytest.fixture
-    def mock_user_api_key_dict(self):
-        """Create a mock user API key dict."""
-        return {"user_id": "test_user"}
-
-    @pytest.mark.asyncio
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.create_pass_through_route")
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_streaming_request_fn")
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.passthrough_endpoint_router")
-    async def test_client_authorization_header_priority(
-        self,
-        mock_router,
-        mock_streaming,
-        mock_create_route,
-        mock_request,
-        mock_response,
-        mock_user_api_key_dict,
-    ):
-        """Test that client Authorization header takes priority over server key."""
-        # Setup
-        mock_request.headers = {"authorization": "Bearer client-key-123"}
-        mock_router.get_credentials.return_value = "server-key-456"
-        mock_streaming.return_value = False
-        mock_endpoint_func = AsyncMock(return_value="test_response")
-        mock_create_route.return_value = mock_endpoint_func
-
-        # Act
-        await anthropic_proxy_route(
-            endpoint="v1/messages",
-            request=mock_request,
-            fastapi_response=mock_response,
-            user_api_key_dict=mock_user_api_key_dict,
-        )
-
-        # Assert
-        mock_create_route.assert_called_once()
-        call_kwargs = mock_create_route.call_args[1]
-
-        assert call_kwargs["custom_headers"] == {}
-        assert call_kwargs["_forward_headers"] is True
-
-    @pytest.mark.asyncio
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.create_pass_through_route")
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_streaming_request_fn")
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.passthrough_endpoint_router")
-    async def test_client_x_api_key_header_priority(
-        self,
-        mock_router,
-        mock_streaming,
-        mock_create_route,
-        mock_request,
-        mock_response,
-        mock_user_api_key_dict,
-    ):
-        """Test that client x-api-key header takes priority over server key."""
-        # Setup
-        mock_request.headers = {"x-api-key": "client-x-api-key-123"}
-        mock_router.get_credentials.return_value = "server-key-456"
-        mock_streaming.return_value = False
-        mock_endpoint_func = AsyncMock(return_value="test_response")
-        mock_create_route.return_value = mock_endpoint_func
-
-        # Act
-        await anthropic_proxy_route(
-            endpoint="v1/messages",
-            request=mock_request,
-            fastapi_response=mock_response,
-            user_api_key_dict=mock_user_api_key_dict,
-        )
-
-        # Assert
-        mock_create_route.assert_called_once()
-        call_kwargs = mock_create_route.call_args[1]
-
-        assert call_kwargs["custom_headers"] == {}
-        assert call_kwargs["_forward_headers"] is True
-
-    @pytest.mark.asyncio
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.create_pass_through_route")
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_streaming_request_fn")
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.passthrough_endpoint_router")
-    async def test_server_api_key_fallback(
-        self,
-        mock_router,
-        mock_streaming,
-        mock_create_route,
-        mock_request,
-        mock_response,
-        mock_user_api_key_dict,
-    ):
-        """Test that server API key is used when no client authentication is provided."""
-        # Setup
-        mock_request.headers = {}  # No authentication headers
-        mock_router.get_credentials.return_value = "server-key-456"
-        mock_streaming.return_value = False
-        mock_endpoint_func = AsyncMock(return_value="test_response")
-        mock_create_route.return_value = mock_endpoint_func
-
-        # Act
-        await anthropic_proxy_route(
-            endpoint="v1/messages",
-            request=mock_request,
-            fastapi_response=mock_response,
-            user_api_key_dict=mock_user_api_key_dict,
-        )
-
-        # Assert
-        mock_create_route.assert_called_once()
-        call_kwargs = mock_create_route.call_args[1]
-
-        assert call_kwargs["custom_headers"] == {"x-api-key": "server-key-456"}
-        assert call_kwargs["_forward_headers"] is True
-
-    @pytest.mark.asyncio
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.create_pass_through_route")
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_streaming_request_fn")
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.passthrough_endpoint_router")
-    async def test_no_authentication_available(
-        self,
-        mock_router,
-        mock_streaming,
-        mock_create_route,
-        mock_request,
-        mock_response,
-        mock_user_api_key_dict,
-    ):
-        """Test that no x-api-key header is added when no authentication is available."""
-        # Setup
-        mock_request.headers = {}  # No authentication headers
-        mock_router.get_credentials.return_value = None  # No server key
-        mock_streaming.return_value = False
-        mock_endpoint_func = AsyncMock(return_value="test_response")
-        mock_create_route.return_value = mock_endpoint_func
-
-        # Act
-        await anthropic_proxy_route(
-            endpoint="v1/messages",
-            request=mock_request,
-            fastapi_response=mock_response,
-            user_api_key_dict=mock_user_api_key_dict,
-        )
-
-        # Assert
-        mock_create_route.assert_called_once()
-        call_kwargs = mock_create_route.call_args[1]
-
-        assert call_kwargs["custom_headers"] == {}
-        assert call_kwargs["_forward_headers"] is True
-
-    @pytest.mark.asyncio
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.create_pass_through_route")
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.is_streaming_request_fn")
-    @patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.passthrough_endpoint_router")
-    async def test_both_client_headers_present(
-        self,
-        mock_router,
-        mock_streaming,
-        mock_create_route,
-        mock_request,
-        mock_response,
-        mock_user_api_key_dict,
-    ):
-        """Test that no server key is added when client has both auth headers."""
-        # Setup
-        mock_request.headers = {
-            "authorization": "Bearer client-auth-key",
-            "x-api-key": "client-x-api-key"
-        }
-        mock_router.get_credentials.return_value = "server-key-456"
-        mock_streaming.return_value = False
-        mock_endpoint_func = AsyncMock(return_value="test_response")
-        mock_create_route.return_value = mock_endpoint_func
-
-        # Act
-        await anthropic_proxy_route(
-            endpoint="v1/messages",
-            request=mock_request,
-            fastapi_response=mock_response,
-            user_api_key_dict=mock_user_api_key_dict,
-        )
-
-        # Assert
-        mock_create_route.assert_called_once()
-        call_kwargs = mock_create_route.call_args[1]
-
-        assert call_kwargs["custom_headers"] == {}
-        assert call_kwargs["_forward_headers"] is True
\ No newline at end of file
diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py
index de7c3a74c2..877a9d069e 100644
--- a/tests/test_litellm/test_router.py
+++ b/tests/test_litellm/test_router.py
@@ -23,7 +23,7 @@ def test_update_kwargs_does_not_mutate_defaults_and_merges_metadata():
             {
                 "model_name": "gpt-3.5-turbo",
                 "litellm_params": {
-                    "model": "azure/chatgpt-v-3",
+                    "model": "azure/gpt-4.1-nano",
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),