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LiteLLM now supports Claude Opus 4.7 on Day 0. Use it across Anthropic, Azure, Vertex AI, and Bedrock through the LiteLLM AI Gateway.

Docker Image

docker pull ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.83.3-stable.opus-4.7

Usage - Anthropic

1. Setup config.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

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-v1.83.3-stable.opus-4.7 \\
  --config /app/config.yaml

3. Test it!

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"
    }
  ]
}'

Usage - Azure

1. Setup config.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

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-v1.83.3-stable.opus-4.7 \\
  --config /app/config.yaml

3. Test it!

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"
    }
  ]
}'

Usage - Vertex AI

1. Setup config.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

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-v1.83.3-stable.opus-4.7 \\
  --config /app/config.yaml

3. Test it!

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"
    }
  ]
}'

Usage - Bedrock

1. Setup config.yaml

model_list:
  - model_name: claude-opus-4-7
    litellm_params:
      model: bedrock/anthropic.claude-opus-4-7
      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

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-v1.83.3-stable.opus-4.7 \\
  --config /app/config.yaml

3. Test it!

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"
    }
  ]
}'

Advanced Features

Adaptive Thinking

:::note When using reasoning_effort with Claude Opus 4.7, all values (low, medium, high, xhigh) are mapped to thinking: {type: "adaptive"}. To use explicit thinking budgets with type: "enabled", pass the native thinking parameter directly. :::

LiteLLM supports adaptive thinking through the reasoning_effort parameter:

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"
}'

Use the thinking parameter with type: "adaptive" to enable adaptive thinking mode:

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."
        }
    ]
}'

Effort Levels

Claude Opus 4.7 supports four effort levels: low, medium, high (default), and xhigh. 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 above high. The max effort level is Claude Opus 4.6 only and is not available on 4.7.

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": "xhigh"
  }
}'

Using OpenAI SDK:

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": "xhigh"}}
)

Using LiteLLM SDK:

from litellm import completion

response = completion(
    model="anthropic/claude-opus-4-7",
    messages=[{"role": "user", "content": "Explain quantum computing"}],
    output_config={"effort": "xhigh"},
)

You can combine reasoning_effort with output_config for even more fine-grained control over the model's behavior.

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": "xhigh"
    }
}'

Effort level guide: