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Claude Code Knowledge Pack7/10/2026

Overview

LiteLLM now supports Claude Opus 4.6 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-v1.80.0-stable.opus-4-6

Usage - Anthropic

1. Setup config.yaml

model_list:
  - model_name: claude-opus-4-6
    litellm_params:
      model: anthropic/claude-opus-4-6
      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-v1.80.0-stable.opus-4-6 \\
  --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-4-6",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

Usage - Azure

1. Setup config.yaml

model_list:
  - model_name: claude-opus-4-6
    litellm_params:
      model: azure_ai/claude-opus-4-6
      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-v1.80.0-stable.opus-4-6 \\
  --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-4-6",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

Usage - Vertex AI

1. Setup config.yaml

model_list:
  - model_name: claude-opus-4-6
    litellm_params:
      model: vertex_ai/claude-opus-4-6
      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-v1.80.0-stable.opus-4-6 \\
  --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-4-6",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

Usage - Bedrock

1. Setup config.yaml

model_list:
  - model_name: claude-opus-4-6
    litellm_params:
      model: bedrock/anthropic.claude-opus-4-6-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

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-v1.80.0-stable.opus-4-6 \\
  --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-4-6",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

Advanced Features

Compaction

Litellm supports enabling compaction for the new claude-opus-4-6.

Enabling Compaction

To enable compaction, add the context_management parameter with the compact_20260112 edit type:

curl --location 'http://0.0.0.0:4000/chat/completions' \\
--header 'Content-Type: application/json' \\
--header 'Authorization: Bearer $LITELLM_KEY' \\
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "What is the weather in San Francisco?"
    }
  ],
  "context_management": {
    "edits": [
      {
        "type": "compact_20260112"
      }
    ]
  },
  "max_tokens": 100
}'

All the parameters supported for context_management by anthropic are supported and can be directly added. Litellm automatically adds the compact-2026-01-12 beta header in the request.

Enable compaction to reduce context size while preserving key information. LiteLLM automatically adds the compact-2026-01-12 beta header when compaction is enabled.

:::info Provider Support: Compaction is supported on Anthropic, Azure AI, and Vertex AI. It is not supported on Bedrock (Invoke or Converse APIs). :::

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-4-6",
    "max_tokens": 4096,
    "messages": [
        {
            "role": "user",
            "content": "Hi"
        }
    ],
    "context_management": {
        "edits": [
            {
                "type": "compact_20260112"
            }
        ]
    }
}'

Response with Compaction Block

The response will include the compaction summary in provider_specific_fields.compaction_blocks:

{
  "id": "chatcmpl-a6c105a3-4b25-419e-9551-c800633b6cb2",
  "created": 1770357619,
  "model": "claude-opus-4-6",
  "object": "chat.completion",
  "choices": [
    {
      "finish_reason": "length",
      "index": 0,
      "message": {
        "content": "I don't have access to real-time data, so I can't provide the current weather in San Francisco. To get up-to-date weather information, I'd recommend checking:\
\
- **Weather websites** like weather.com, accuweather.com, or wunderground.com\
- **Search engines** – just Google \\"San Francisco weather\\"\
- **Weather apps** on your phone (e.g., Apple Weather, Google Weather)\
- **National",
        "role": "assistant",
        "provider_specific_fields": {
          "compaction_blocks": [
            {
              "type": "compaction",
              "content": "Summary of the conversation: The user requested help building a web scraper..."
            }
          ]
        }
      }
    }
  ],
  "usage": {
    "completion_tokens": 100,
    "prompt_tokens": 86,
    "total_tokens": 186
  }
}

Using Compaction Blocks in Follow-up Requests

To continue the conversation with compaction, include the compaction block in the assistant message's provider_specific_fields:

curl --location 'http://0.0.0.0:4000/chat/completions' \\
--header 'Content-Type: application/json' \\
--header 'Authorization: Bearer $LITELLM_KEY' \\
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "How can I build a web scraper?"
    },
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "Certainly! To build a basic web scraper, you'll typically use a programming language like Python along with libraries such as `requests` (for fetching web pages) and `BeautifulSoup` (for parsing HTML). Here's a basic example:\
\
```python\

from bs4 import BeautifulSoup\
\
url = 'https://example.com'\
response = requests.get(url)\
soup = BeautifulSoup(response.text, 'html.parser')\
\
# Extract and print all text\
text = soup.get_text()\
print(text)\
```\
\
Let me know what you're interested in scraping or if you need help with a specific website!"
        }
      ],
      "provider_specific_fields": {
        "compaction_blocks": [
          {
            "type": "compaction",
            "content": "Summary of the conversation: The user asked how to build a web scraper, and the assistant gave an overview using Python with requests and BeautifulSoup."
          }
        ]
      }
    },
    {
      "role": "user",
      "content": "How do I use it to scrape product prices?"
    }
  ],
  "context_management": {
    "edits": [
      {
        "type": "compact_20260112"
      }
    ]
  },
  "max_tokens": 100
}'

Streaming Support

Compaction blocks are also supported in streaming mode. You'll receive:

  • compaction_start event when a compaction block begins
  • compaction_delta events with the compaction content
  • The accumulated compaction_blocks in provider_specific_fields

Adaptive Thinking

:::note When using reasoning_effort with Claude Opus 4.6, all values (low, medium, high) are mapped to thinking: {type: "adaptive"}. To use explicit thinking budgets with type: "enabled", pass the native thinking parameter directly (see "Native thinking param" tab below). :::

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-4-6",
  "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-4-6",
    "max_tokens": 16000,
    "thinking": {
        "type": "adaptive"
    },
    "messages": [
        {
            "role": "user",
            "content": "Explain why the sum of two even numbers is always even."
        }
    ]
}'

Use the thinking parameter directly for adaptive thinking via the SDK:


response = litellm.completion(
  model="anthropic/claude-opus-4-6",
  messages=[{"role": "user", "content": "Solve this complex problem: What is the optimal strategy for..."}],
  thinking={"type": "adaptive"},
)

Effort Levels

Four effort levels available: low, medium, high (default), and max. Pass directly via the output_config 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-4-6",
  "messages": [
    {
      "role": "user",
      "content": "Explain quantum computing"
    }
  ],
  "output_config": {
        "effort": "medium"
    }
}'

You can use reasoning effort plus output_config to have more control on the model.

Four effort levels available: low, medium, high (default), and max. Pass directly via the output_config parameter:

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-4-6",
    "max_tokens": 4096,
    "messages": [
        {
            "role": "user",
            "content": "Explain quantum computing"
        }
    ],
    "output_config": {
        "effort": "medium"
    }
}'

1M Token Context (Beta)

Opus 4.6 supports 1M token context. Premium pricing applies for prompts exceeding 200k tokens ($10/$37.50 per million input/output tokens). LiteLLM supports cost calculations for 1M token contexts.

To use the 1M token context window, you need to forward the anthropic-beta header from your client to the LLM provider.

Step 1: Enable header forwarding in your config

general_settings:
  forward_client_headers_to_llm_api: true

Step 2: Send requests with the beta header

curl --location 'http://0.0.0.0:4000/chat/completions' \\
--header 'Content-Type: application/json' \\
--header 'Authorization: Bearer $LITELLM_KEY' \\
--header 'anthropic-beta: context-1m-2025-08-07' \\
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "Analyze this large document..."
    }
  ]
}'

To use the 1M token context window, you need to forward the anthropic-beta header from your client to the LLM provider.

Step 1: Enable header forwarding in your config

general_settings:
  forward_client_headers_to_llm_api: true

Step 2: Send requests with the beta header

curl --location 'http://0.0.0.0:4000/v1/messages' \\
--header 'x-api-key: sk-12345' \\
--header 'anthropic-beta: context-1m-2025-08-07' \\
--header 'content-type: application/json' \\
--data '{
    "model": "claude-opus-4-6",
    "max_tokens": 16000,
    "messages": [
        {
            "role": "user",
            "content": "Analyze this large document..."
        }
    ]
}'

:::tip You can combine multiple beta headers by separating them with commas:

--header 'anthropic-beta: context-1m-2025-08-07,compact-2026-01-12'

:::

US-Only Inference

Available at 1.1× token pricing. LiteLLM automatically tracks costs for US-only inference.

Use the inference_geo parameter to specify US-only inference:

curl --location 'http://0.0.0.0:4000/chat/completions' \\
--header 'Content-Type: application/json' \\
--header 'Authorization: Bearer $LITELLM_KEY' \\
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "What is the capital of