- Adjusted input and output cost per token for existing models.
- Added new model configuration for "openrouter/qwen/qwen3-coder" with specified token limits and costs.
* fix(main.py): fix async retryer
Fixes https://github.com/BerriAI/litellm/issues/12830
* fix(forward_clientside_headers_by_model_group.py): filter out 'content-type' from forwardable headers
clientside content-type != proxy content type, can cause requests to hang
* test(tests/): update tests
* feat: add Morph provider support
- Add MorphChatConfig implementation for OpenAI-compatible API
- Support morph-v3-fast and morph-v3-large models
- Add pricing: morph-v3-fast (/bin/zsh.8/.2 per 1M tokens), morph-v3-large (/bin/zsh.9/.9 per 1M tokens)
- Both models support 16k context window and system messages
- Add comprehensive documentation and unit tests
- Update all necessary integration points (constants, init, provider logic)
* feat: Add Morph provider support in ProviderConfigManager
- Extend ProviderConfigManager to include MorphChatConfig for the Morph LLM provider.
- Update MorphChatConfig by removing unused parameters from the configuration.
- Add Hyperbolic as a new OpenAI-compatible provider
- Implement HyperbolicChatConfig inheriting from OpenAILikeChatConfig
- Register Hyperbolic in provider lists and constants
- Add comprehensive model configurations with pricing for:
- DeepSeek models (V3, R1, etc.)
- Qwen models (2.5, 3, QwQ, etc.)
- Meta Llama models (3.1, 3.2, 3.3)
- Other models like Kimi K2, Hermes 3, etc.
- Configure default API base URL: https://api.hyperbolic.xyz/v1
- Add provider documentation with usage examples
- Create unit tests for provider functionality
- Support all standard OpenAI parameters
Hyperbolic provides low-cost inference with OpenAI-compatible APIs,
supporting latest models without infrastructure overhead.
* feat: add Lambda AI provider support
Add support for Lambda AI (lambda.ai) as a new LLM provider in LiteLLM. Lambda AI provides access to a wide range of open-source models through their cloud GPU infrastructure.
Changes:
- Add Lambda AI provider implementation (OpenAI-compatible)
- Register 20 Lambda AI models with accurate pricing and 131k context windows
- Add comprehensive tests for Lambda AI integration
- Add detailed documentation with usage examples
- Use "lambda_ai" as provider name to avoid Python keyword conflict
Models include Llama 3.x, DeepSeek, Hermes, Qwen, and specialized models for coding and vision tasks.
* fix(tests): ensure lambda_ai_models list is repopulated after model cost reload
Updated test cases to clear and repopulate the lambda_ai_models list after reloading the model cost map. This ensures that the tests accurately reflect the current state of available models.
* feat: add Lambda AI chat configuration support
Added support for Lambda AI chat configuration in the ProviderConfigManager. This enhancement allows the integration of Lambda AI as a provider, expanding the capabilities of LiteLLM.
* Feature/track bedrock gov cloud models (#12771)
* feat: add AWS Bedrock GovCloud model support (LIT-257)
- Added 18 GovCloud-specific model entries (9 per region) to model_prices_and_context_window.json
- Updated is_bedrock_pricing_only_model() to allow GovCloud models (us-gov-east-1, us-gov-west-1)
- Added comprehensive test suite for GovCloud model support
- Ensures GovCloud models use appropriate APIs (Converse for Claude/Llama, Invoke for Titan)
Models added:
- Claude 3.5 Sonnet and Claude 3 Haiku (FedRAMP/IL4/5 approved)
- Llama 3 8B and 70B (FedRAMP/IL4/5 approved)
- Amazon Titan Text and Embedding models
* fix: add bedrock_converse GovCloud model mappings for Claude models
Added missing bedrock_converse model entries for AWS GovCloud regions:
- bedrock_converse/us-gov-east-1/anthropic.claude-3-5-sonnet-20240620-v1:0
- bedrock_converse/us-gov-east-1/anthropic.claude-3-haiku-20240307-v1:0
- bedrock_converse/us-gov-west-1/anthropic.claude-3-5-sonnet-20240620-v1:0
- bedrock_converse/us-gov-west-1/anthropic.claude-3-haiku-20240307-v1:0
This fixes test failures where supports_tool_choice() returned True but
the models weren't properly mapped in the configuration files.
* fix: correct AWS GovCloud Bedrock model pricing and configurations
- Fix Claude 3.5 Sonnet pricing (3.6e-06 input, 1.8e-05 output)
- Fix Claude 3 Haiku pricing (3e-07 input, 1.5e-06 output)
- Update Claude 3.5 Sonnet max_tokens from 4096 to 8192
- Add bedrock_converse entries for Llama models with correct token limits
- Add Amazon Nova Pro model for both GovCloud regions
- Add supports_pdf_input flag to Claude models
* fix: handle bedrock_converse prefix in get_non_litellm_routing_model_name
Fixes test failure where bedrock_converse/region/model paths were not properly
stripped to get the base model name, causing supports_function_calling to
return false for regional bedrock_converse models.
* revert: reset bedrock/common_utils.py to match main branch
Remove bedrock_converse prefix handling from get_non_litellm_routing_model_name
to align with main branch implementation.
* revert: reset litellm/__init__.py to match main branch
- Remove public_model_groups variables
- Remove GovCloud exception handling in is_bedrock_pricing_only_model
- Fix comment formatting
* revert: reset litellm/__init__.py to exact main branch content
Copy exact content from origin/main with no modifications
* fix: remove bedrock_converse prefixed models from pricing files
- Remove 10 bedrock_converse entries from model_prices_and_context_window.json
- Remove 4 bedrock_converse entries from litellm/model_prices_and_context_window_backup.json
- These were GovCloud-specific entries that are no longer needed
* fix: correct AWS GovCloud Bedrock model pricing and configurations
- Fix Anthropic Claude 3.5 Sonnet pricing: $3.60/$18.00 per million tokens (was $3.00/$15.00)
- Fix Anthropic Claude 3 Haiku pricing: $0.30/$1.50 per million tokens (was $0.25/$1.25)
- Fix Claude 3.5 Sonnet max_tokens: 8192 (was 4096)
- Fix Llama model max_tokens: 2048 (was 8192) and max_input_tokens: 8000 (was 8192)
- Fix Llama3-8b output pricing: $2.65 per million tokens (was $0.60)
- Add missing Amazon Nova Pro models for both GovCloud regions
- Add supports_pdf_input flag to Llama models
Based on official AWS Bedrock pricing documentation for GovCloud regions
* test: fix GovCloud bedrock models test to match implementation
Update test_govcloud_model_in_bedrock_models_list to correctly verify that
GovCloud models are excluded from bedrock_models list as they are
pricing-only models following the bedrock/<region>/<model> pattern.
---------
Co-authored-by: Cole McIntosh <colemcintosh6@gmail.com>
Co-authored-by: Cole McIntosh <82463175+colesmcintosh@users.noreply.github.com>
* add tests
* add tests
* Added test costs
* Added test costs
---------
Co-authored-by: Cole McIntosh <colemcintosh6@gmail.com>
Co-authored-by: Cole McIntosh <82463175+colesmcintosh@users.noreply.github.com>
- Changed groq/moonshotai-kimi-k2-instruct to groq/moonshotai/kimi-k2-instruct in model_prices_and_context_window.json
- Added groq/moonshotai/kimi-k2-instruct and groq/qwen-qwq-32b to the supported models table in Groq documentation
* feat: add v0 provider support to LiteLLM
- Add v0 as a new OpenAI-compatible provider
- Support all three v0 models: v0-1.0-md, v0-1.5-md, v0-1.5-lg
- Configure correct token limits and pricing for each model
- Enable vision support for all v0 models (multimodal)
- Add provider detection for v0/ prefix and api.v0.dev endpoint
- Include comprehensive unit tests for the provider
The v0 provider uses the standard OpenAI-compatible implementation
and supports all standard features including streaming, function
calling, and system messages.
* fix: add v0 provider to ProviderConfigManager
Add V0ChatConfig to the get_provider_chat_config method to fix
test_supports_tool_choice test failure. The v0 provider needs to
be included in the provider config manager to return the correct
configuration for tool choice support detection.
* docs: add documentation for v0 provider
- Add comprehensive v0 provider documentation
- Cover all supported models and their capabilities
- Include examples for SDK usage, proxy configuration, and all features
- Document supported OpenAI parameters based on v0 API docs
- Add v0 to the providers sidebar navigation
* fix: correct v0 supported OpenAI parameters
Based on review feedback and v0 API documentation:
- v0 only supports: messages, model, stream, tools, tool_choice
- Remove unsupported parameters like temperature, max_tokens, etc.
- Update tests to verify correct parameter set
- Update documentation to reflect actual API capabilities
- Remove JSON mode example as response_format is not supported
Reference: https://v0.dev/docs/v0-model-api#request-body
* fix: remove supports_response_schema from v0 models
Remove the supports_response_schema property from all v0 models in the model configuration files as v0 does not support this feature.
Models updated:
- v0/v0-1.0-md
- v0/v0-1.5-md
- v0/v0-1.5-lg
Added cache_read_input_token_cost (25% of regular input cost) to all 39 Gemini 2.x models
to properly support implicit context caching cost calculations. Previously, cached tokens
were being charged at full price instead of the discounted rate.
Fixes#11156
* [Feat] New LLM API Integration - Add Moonshot API (Kimi) (#12551)
* Add Moonshot AI provider support to LiteLLM
Co-authored-by: ishaan <ishaan@berri.ai>
* Refactor Moonshot provider params handling and transformation logic
Co-authored-by: ishaan <ishaan@berri.ai>
* fix constants
* add Moonshot AI
* fix get_supported_openai_params
* handle kimi temp
* add tool choice handling
* test moonshot unit tests
* fix kimi
* fix linting
* Add pricing information for Moonshot AI's kimi-k2 model (#12566)
* Add pricing information for Moonshot AI's kimi-k2 model
* Update model name to kimi-k2-0711-preview
- Changed model name from moonshot/kimi-k2 to moonshot/kimi-k2-0711-preview
- This reflects the specific model version as requested
* Update moonshot_models list to match model_context JSON
---------
Co-authored-by: openhands <openhands@all-hands.dev>
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: ishaan <ishaan@berri.ai>
Co-authored-by: Xingyao Wang <xingyao@all-hands.dev>
Co-authored-by: openhands <openhands@all-hands.dev>
* update docs
* docs moonshot
* fixes model cost map
* fix map_openai_params
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: ishaan <ishaan@berri.ai>
Co-authored-by: Xingyao Wang <xingyao@all-hands.dev>
Co-authored-by: openhands <openhands@all-hands.dev>
* Added dashscope as a provider
* Fix some leftover references on nebius
* Porting the dashscope api endpoit international version
* explicit tool_choice = True in config
- Add azure/o3-deep-research model to pricing configuration
- Input cost: 0 per million tokens (1e-05 per token)
- Output cost: 0 per million tokens (4e-05 per token)
- Supports reasoning, web search, function calling, vision, and other advanced features
- Based on Azure AI Foundry Deep Research pricing documentation
Resolves#12318
Co-authored-by: openhands <openhands@all-hands.dev>
* build(model_prices_and_context_window.json): remove 'supports_tool_choice' for specific mistral models
Closes https://github.com/BerriAI/litellm/issues/11750
* feat: initial commit adding cleaner ui for azure text moderation guardrails
* feat(guardrail_endpoints.py): add discoverable guardrail configs and improve converting base model to dict with types
* fix(guardrail_provider_fields.tsx): render from api endpoint correctly
* fix(guardrail_provider_fields.tsx): cleanup
* refactor(guardrail_endpoints.py): refactor to handle dictionaries with literal - allows multiselect
* feat(ui/): render dictionary with known keys correctly
* feat(ui/): render optional params on separate page
* style(ui/): style improvements to rendering optional params on the UI
* feat(azure/prompt_shield.py): add azure prompt shield back on UI
* fix(add_guardrail_form.tsx): fix form to handle updated api
* fix(guardrail_optional_params.tsx): ensure values are nested correctly for writing to api
* fix: fix linting error
* test: update tests
* fix mapped tests (#12320)
* fix - use flush llm client cache
* faster mapped tests
* test_async_multiple_response_ids_routing
* fix tests
* test_ateam_member_update_admin_requires_premium
* regular mapped tests
* Revert "Fix: Initialize JSON logging for all loggers when JSON_LOGS=True (#12206)"
This reverts commit 2c60c316ec.
* reset num workers
* check_for_litellm_module_deletion
* add test
* add code_qa_check_tests
* ci/cd new release
* docs(opentelemetry_integration.md): add otel debug flag to docs
help people debug their traces easily
* [Feat] Add failure logging support for s3 logger (#12299)
* add async_log_failure_event
* test_basic_s3_v2_logging_failure
* Comma separated spend and budget (#12317)
* all_keys_table, key_info_view, organizations
* teams
* the whole usage page
* teams page
* users table
* user info
* more commas
* some more commas
* Segregate MCP tools on connections using headers (#12296)
* Add get tools segregation
* add ui changes (#12302)
* resolve comments
* add mapped tests
* remove advanced settings (#12323)
* [Bug Fix] /generateContent API - Only pass supported params when using OpenAI models (#12297)
* fix - only pass GenericLiteLLMParams
* test_google_generate_content_with_openai
* Fix: Fix custom ca bundle support in aiohttp transport (#12281)
* Unify usage of get_ssl_configuration
* Fix doc
* [Feat] Add github co-pilot as a new LLM API provider (#12325)
* Litellm dev 03 05 2025 contributor prs (#9079)
* feat: add support for copilot provider
* test: add tests for github copilot
* chore: clean up github copilot authenticator
* test: add test for github copilot authenticator
* test: add test for github copilot for sonnet 3.7 thought model
* Fix#7629 - Add tzdata package to Dockerfile (#8915)
* Add tzdata package to Dockerfile
* Move tzdata to python requirement.txt
* feat: add support for copilot provider (#8577)
* feat: add support for copilot provider
* test: add tests for github copilot
* chore: clean up github copilot authenticator
* test: add test for github copilot authenticator
* test: add test for github copilot for sonnet 3.7 thought model
---------
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* feat: add model information for copilot models
* fix: fix linting errors
* test: remove integration test for github_copilot + fix misisng mock
* fix: use print to make sure the logger message shown
* test: remove debug print
* fix lint (#11112)
* Add init files to make test directories Python packages and update import paths in test_token_counter.py (#11119)
* Update litellm/model_prices_and_context_window_backup.json
Co-authored-by: மனோஜ்குமார் பழனிச்சாமி <smartmanoj42857@gmail.com>
---------
Co-authored-by: Son H. Nguyen <nhs.000.dev@gmail.com>
Co-authored-by: subnet.dev <50828879+subnet-dev@users.noreply.github.com>
Co-authored-by: Son H. Nguyen <33925625+nhs000@users.noreply.github.com>
Co-authored-by: மனோஜ்குமார் பழனிச்சாமி <smartmanoj42857@gmail.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* refactor github copilot
* test_github_copilot_transformation.py
* test_github_copilot_authenticator.py
* add GitHub Copilot
* fix order
* doc fix
---------
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Co-authored-by: Son H. Nguyen <nhs.000.dev@gmail.com>
Co-authored-by: subnet.dev <50828879+subnet-dev@users.noreply.github.com>
Co-authored-by: Son H. Nguyen <33925625+nhs000@users.noreply.github.com>
Co-authored-by: மனோஜ்குமார் பழனிச்சாமி <smartmanoj42857@gmail.com>
* add new env vars to docs
* ssl_cert_error (#12327)
* feat - add new banner on startup (#12328)
* [Security Bug Fix] Ensure only LLM API route fails get logged on Langfuse (and other loggers) (#12308)
* _is_proxy_only_llm_api_error
* test_proxy_only_error_true_for_llm_route
* add not on change
* Update tests/test_litellm/proxy/test_proxy_utils.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* add test_post_call_failure_hook_auth_error_key_info_route
* test fix _is_proxy_only_llm_api_error
* test_chat_completion_request_with_redaction
* test_post_call_failure_hook_auth_error_llm_api_route
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Ensure message redaction works for responses API logging (#12291)
* add fixes to choice implementation redaction
* add isInstance check on responses API
* change datadog to revert back
* change datadog to revert back
* fix type errors
* Redaction test changes
* Redaction test changes
* Redaction test changes - remove changes
* [Feat] UI - Allow Viewing/Editing Team Based Callbacks (#12329)
* add logging settings view on UI
* fix change ordering
* fix type error
* test fix - import loc
* [Feat] QA - Arize Team based logging (#12331)
* add _get_tracer_with_dynamic_headers
* fix construct_dynamic_arize_headers
* [Feat] UI - Allow Viewing/Editing Team Based Callbacks (#12329)
* add logging settings view on UI
* fix change ordering
* add construct_dynamic_otel_headers for arize
* refactor common code
* test_construct_dynamic_arize_headers
* otel unit tests
* test_arize_dynamic_params
* test_arize_dynamic_headers_in_grpc_requests
* [Feat] UI - Allow setting Logging Callback Setting per Key (#12333)
* allow adding logging settings on a key
* add ability to edit key logging settings
* dont leak api keys on ui
* fix order
* add langsmith settings
* ui new build
* test_default_api_base
* fix new utils tests
* bump: version 1.73.7 → 1.74.0
* Add mcp server segregation comma separated support (#12326)
* add mcp comma separated support
* dont support legacy json array
* lint
* add live tail state to session storage (#12335)
* add aiml
* fix: fix unused imports
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: tanjiro <56165694+NANDINI-star@users.noreply.github.com>
Co-authored-by: Jugal D. Bhatt <55304795+jugaldb@users.noreply.github.com>
Co-authored-by: Joost van Doorn <joost.van.doorn@gmail.com>
Co-authored-by: Son H. Nguyen <nhs.000.dev@gmail.com>
Co-authored-by: subnet.dev <50828879+subnet-dev@users.noreply.github.com>
Co-authored-by: Son H. Nguyen <33925625+nhs000@users.noreply.github.com>
Co-authored-by: மனோஜ்குமார் பழனிச்சாமி <smartmanoj42857@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* add watsonx/mistralai/mistral-large to model prices and context window json
- Set max_tokens and max_input_tokens to 131072 and max_output_tokens to 16384 to match IBM's official context window limit for Mistral Large models hosted on watsonx.
- Correct input_cost_per_token and output_cost_per_token to reflect IBM's pricing of $0.003 and $0.01 per 1000 tokens, respectively, converted to per-token rates (0.000003 and 0.00001).
- Confirmed function-calling support based on IBM documentation and Mistral's own API schema.
- Aligns with Mistral's own model entries that specify token windows of ~128K and matches LiteLLM's expected cost format.
**Sources:**
- IBM watsonx Foundation Models docs: https://www.ibm.com/docs/en/watsonx/w-and-w/2.1.x?topic=models-foundation-model-details#mistral-large
- IBM Pricing information for watsonx.ai models: https://www.ibm.com/products/watsonx-ai/pricing
- LiteLLM repo docs on token cost formatting and limits https://docs.litellm.ai/docs/proxy/custom_pricing.
- Imitating entries from litellm/model_prices_and_context_window.json for watsonx/ibm/granite-3-8b-instruct as well as Mistral's own mistral/mistral-large-latest and similar.
* Update model_prices_and_context_window.json
typo
* Update model_prices_and_context_window.json
* Update model_prices_and_context_window.json
