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docs/my-website/blog/claude_opus_4_7/index.md
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---
slug: claude_opus_4_7
title: "Day 0 Support: Claude Opus 4.7"
date: 2026-04-16T10:00:00
authors:
- sameer
- ishaan-alt
- krrish
description: "Day 0 support for Claude Opus 4.7 on LiteLLM AI Gateway - use across Anthropic, Azure, Vertex AI, and Bedrock."
tags: [anthropic, claude, opus 4.7]
hide_table_of_contents: false
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
LiteLLM now supports [Claude Opus 4.7](https://www.anthropic.com/news/claude-opus-4-7) on Day 0. Use it across Anthropic, Azure, Vertex AI, and Bedrock through the LiteLLM AI Gateway.
{/* truncate */}
## Docker Image
```bash
docker pull ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.82.0-stable.opus-4-7
```
## Usage - Anthropic
<Tabs>
<TabItem value="proxy" label="LiteLLM Proxy">
**1. Setup config.yaml**
```yaml
model_list:
- model_name: claude-opus-4-7
litellm_params:
model: anthropic/claude-opus-4-7
api_key: os.environ/ANTHROPIC_API_KEY
```
**2. Start the proxy**
```bash
docker run -d \
-p 4000:4000 \
-e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
-v $(pwd)/config.yaml:/app/config.yaml \
ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.82.0-stable.opus-4-7 \
--config /app/config.yaml
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
"model": "claude-opus-4-7",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
```
</TabItem>
</Tabs>
## Usage - Azure
<Tabs>
<TabItem value="proxy" label="LiteLLM Proxy">
**1. Setup config.yaml**
```yaml
model_list:
- model_name: claude-opus-4-7
litellm_params:
model: azure_ai/claude-opus-4-7
api_key: os.environ/AZURE_AI_API_KEY
api_base: os.environ/AZURE_AI_API_BASE # https://<resource>.services.ai.azure.com
```
**2. Start the proxy**
```bash
docker run -d \
-p 4000:4000 \
-e AZURE_AI_API_KEY=$AZURE_AI_API_KEY \
-e AZURE_AI_API_BASE=$AZURE_AI_API_BASE \
-v $(pwd)/config.yaml:/app/config.yaml \
ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.82.0-stable.opus-4-7 \
--config /app/config.yaml
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
"model": "claude-opus-4-7",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
```
</TabItem>
</Tabs>
## Usage - Vertex AI
<Tabs>
<TabItem value="proxy" label="LiteLLM Proxy">
**1. Setup config.yaml**
```yaml
model_list:
- model_name: claude-opus-4-7
litellm_params:
model: vertex_ai/claude-opus-4-7
vertex_project: os.environ/VERTEX_PROJECT
vertex_location: us-east5
```
**2. Start the proxy**
```bash
docker run -d \
-p 4000:4000 \
-e VERTEX_PROJECT=$VERTEX_PROJECT \
-e GOOGLE_APPLICATION_CREDENTIALS=/app/credentials.json \
-v $(pwd)/config.yaml:/app/config.yaml \
-v $(pwd)/credentials.json:/app/credentials.json \
ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.82.0-stable.opus-4-7 \
--config /app/config.yaml
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
"model": "claude-opus-4-7",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
```
</TabItem>
</Tabs>
## Usage - Bedrock
<Tabs>
<TabItem value="proxy" label="LiteLLM Proxy">
**1. Setup config.yaml**
```yaml
model_list:
- model_name: claude-opus-4-7
litellm_params:
model: bedrock/anthropic.claude-opus-4-7-v1
aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
aws_region_name: us-east-1
```
**2. Start the proxy**
```bash
docker run -d \
-p 4000:4000 \
-e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
-e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
-v $(pwd)/config.yaml:/app/config.yaml \
ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.82.0-stable.opus-4-7 \
--config /app/config.yaml
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
"model": "claude-opus-4-7",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
```
</TabItem>
</Tabs>
## Advanced Features
### Adaptive Thinking
:::note
When using `reasoning_effort` with Claude Opus 4.7, all values (`low`, `medium`, `high`, `xhigh`, `max`) are mapped to `thinking: {type: "adaptive"}`. To use explicit thinking budgets with `type: "enabled"`, pass the native `thinking` parameter directly.
:::
<Tabs>
<TabItem value="completions" label="/chat/completions">
LiteLLM supports adaptive thinking through the `reasoning_effort` parameter:
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
"model": "claude-opus-4-7",
"messages": [
{
"role": "user",
"content": "Solve this complex problem: What is the optimal strategy for..."
}
],
"reasoning_effort": "high"
}'
```
</TabItem>
<TabItem value="messages" label="/v1/messages">
Use the `thinking` parameter with `type: "adaptive"` to enable adaptive thinking mode:
```bash
curl --location 'http://0.0.0.0:4000/v1/messages' \
--header 'x-api-key: sk-12345' \
--header 'content-type: application/json' \
--data '{
"model": "claude-opus-4-7",
"max_tokens": 16000,
"thinking": {
"type": "adaptive"
},
"messages": [
{
"role": "user",
"content": "Explain why the sum of two even numbers is always even."
}
]
}'
```
</TabItem>
</Tabs>
### Effort Levels
Claude Opus 4.7 supports five effort levels: `low`, `medium`, `high` (default), `xhigh`, and `max`. These give you finer-grained control over how much reasoning the model applies to a task. Pass the effort level via the `output_config` parameter.
`xhigh` is a new effort level introduced with Opus 4.7 that sits between `high` and `max`. `max` is available on Claude Opus 4.6 and 4.7 only.
<Tabs>
<TabItem value="completions" label="/chat/completions">
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
"model": "claude-opus-4-7",
"messages": [
{
"role": "user",
"content": "Explain quantum computing"
}
],
"output_config": {
"effort": "max"
}
}'
```
**Using OpenAI SDK:**
```python
import openai
client = openai.OpenAI(
api_key="your-litellm-key",
base_url="http://0.0.0.0:4000"
)
response = client.chat.completions.create(
model="claude-opus-4-7",
messages=[{"role": "user", "content": "Explain quantum computing"}],
extra_body={"output_config": {"effort": "max"}}
)
```
**Using LiteLLM SDK:**
```python
from litellm import completion
response = completion(
model="anthropic/claude-opus-4-7",
messages=[{"role": "user", "content": "Explain quantum computing"}],
output_config={"effort": "max"},
)
```
You can combine `reasoning_effort` with `output_config` for even more fine-grained control over the model's behavior.
</TabItem>
<TabItem value="messages" label="/v1/messages">
```bash
curl --location 'http://0.0.0.0:4000/v1/messages' \
--header 'x-api-key: sk-12345' \
--header 'content-type: application/json' \
--data '{
"model": "claude-opus-4-7",
"max_tokens": 4096,
"messages": [
{
"role": "user",
"content": "Explain quantum computing"
}
],
"output_config": {
"effort": "max"
}
}'
```
</TabItem>
</Tabs>
**Effort level guide:**
| Effort | When to use |
|--------|-------------|
| `low` | Short, fast responses — simple lookups, formatting, classification |
| `medium` | Balanced tradeoff for everyday Q&A and light reasoning |
| `high` (default) | Complex reasoning, code generation, analysis |
| `xhigh` | Harder reasoning than `high` when `max` is overkill — tough debugging, non-trivial planning |
| `max` | Hardest problems — multi-step math, deep research, agentic planning (Opus 4.6 and 4.7 only) |
litellm/anthropic_beta_headers_config.json
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@@ -71,12 +71,12 @@
"computer-use-2025-01-24": "computer-use-2025-01-24",
"computer-use-2025-11-24": "computer-use-2025-11-24",
"context-1m-2025-08-07": "context-1m-2025-08-07",
"context-management-2025-06-27": "context-management-2025-06-27",
"context-management-2025-06-27": null,
"effort-2025-11-24": null,
"fast-mode-2026-02-01": null,
"files-api-2025-04-14": null,
"fine-grained-tool-streaming-2025-05-14": null,
"interleaved-thinking-2025-05-14": "interleaved-thinking-2025-05-14",
"interleaved-thinking-2025-05-14": null,
"mcp-client-2025-11-20": null,
"mcp-client-2025-04-04": null,
"mcp-servers-2025-12-04": null,
@@ -102,12 +102,12 @@
"computer-use-2025-01-24": "computer-use-2025-01-24",
"computer-use-2025-11-24": "computer-use-2025-11-24",
"context-1m-2025-08-07": "context-1m-2025-08-07",
"context-management-2025-06-27": "context-management-2025-06-27",
"context-management-2025-06-27": null,
"effort-2025-11-24": null,
"fast-mode-2026-02-01": null,
"files-api-2025-04-14": null,
"fine-grained-tool-streaming-2025-05-14": null,
"interleaved-thinking-2025-05-14": "interleaved-thinking-2025-05-14",
"interleaved-thinking-2025-05-14": null,
"mcp-client-2025-11-20": null,
"mcp-client-2025-04-04": null,
"mcp-servers-2025-12-04": null,
litellm/llms/anthropic/chat/transformation.py
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@@ -1534,13 +1534,20 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
f"Invalid effort value: {effort}. Must be one of: "
f"'high', 'medium', 'low', 'xhigh', 'max'"
)
# xhigh / max are opt-in effort levels; gate them on the model map
# capability flag (``supports_xhigh_reasoning_effort`` / ``supports_max_reasoning_effort``).
if effort in ("xhigh", "max") and not self._supports_effort_level(
model, effort
# ``max`` is Claude Opus 4.6 and 4.7 only (not Sonnet 4.6, not Opus 4.5).
# Keep this hardcoded so the error message is specific and stable.
if effort == "max" and not (
self._is_opus_4_6_model(model) or self._is_opus_4_7_model(model)
):
raise ValueError(
f"effort={effort!r} is not supported by this model. Got model: {model}"
f"effort='max' is only supported by Claude Opus 4.6 and 4.7. "
f"Got model: {model}"
)
# ``xhigh`` is data-driven via ``supports_xhigh_reasoning_effort`` so
# enabling it for a new model is a pure model-map change.
if effort == "xhigh" and not self._supports_effort_level(model, "xhigh"):
raise ValueError(
f"effort='xhigh' is not supported by this model. Got model: {model}"
)
data["output_config"] = output_config
tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py
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@@ -432,7 +432,10 @@ def test_bedrock_invoke_messages_skips_thinking_injection_when_already_enabled()
litellm_params=GenericLiteLLMParams(),
headers={},
)
assert result["thinking"]["budget_tokens"] == 2048
# Claude 4.6/4.7 reject ``thinking.type=enabled``; legacy ``enabled`` is
# translated to ``adaptive`` (budget_tokens => output_config.effort) and the
# pre-4.6 ``interleaved-thinking-2025-05-14`` beta must not be attached.
assert result["thinking"]["type"] == "adaptive"
betas = result.get("anthropic_beta") or []
assert "interleaved-thinking-2025-05-14" not in betas
tests/test_litellm/test_utils.py
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@@ -771,6 +771,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
"supports_minimal_reasoning_effort": {"type": "boolean"},
"supports_none_reasoning_effort": {"type": "boolean"},
"supports_xhigh_reasoning_effort": {"type": "boolean"},
"supports_max_reasoning_effort": {"type": "boolean"},
"supports_service_tier": {"type": "boolean"},
"supports_preset": {"type": "boolean"},
"tool_use_system_prompt_tokens": {"type": "number"},