--- title: "index 52" description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/index-52 source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:25:31.017Z license: CC-BY-4.0 attribution: "index 52 โ€” Claudary (https://claudary.paisolsolutions.com/skills/index-52)" --- # index 52 import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; ## Overview --- 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-v1.83.3-stable.opus-4.7 ``` ## Usage - Anthropic **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-v1.83.3-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 $LITELLM_KEY' \\ --data '{ "model": "claude-opus-4-7", "messages": [ { "role": "user", "content": "what llm are you" } ] }' ``` ## Usage - Azure **1. Setup config.yaml** ```yaml model_list: - model_name: claude-opus-4-7 litellm_params: model: azure_ai/claude-opus-4-7 api_key: os.environ/AZURE_AI_API_KEY api_base: os.environ/AZURE_AI_API_BASE # https://.services.ai.azure.com ``` **2. Start the proxy** ```bash docker run -d \\ -p 4000:4000 \\ -e AZURE_AI_API_KEY=$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-v1.83.3-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 $LITELLM_KEY' \\ --data '{ "model": "claude-opus-4-7", "messages": [ { "role": "user", "content": "what llm are you" } ] }' ``` ## Usage - Vertex AI **1. Setup config.yaml** ```yaml model_list: - model_name: claude-opus-4-7 litellm_params: model: vertex_ai/claude-opus-4-7 vertex_project: os.environ/VERTEX_PROJECT vertex_location: us-east5 ``` **2. Start the proxy** ```bash docker run -d \\ -p 4000:4000 \\ -e VERTEX_PROJECT=$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-v1.83.3-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 $LITELLM_KEY' \\ --data '{ "model": "claude-opus-4-7", "messages": [ { "role": "user", "content": "what llm are you" } ] }' ``` ## Usage - Bedrock **1. Setup config.yaml** ```yaml model_list: - model_name: claude-opus-4-7 litellm_params: model: bedrock/anthropic.claude-opus-4-7 aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY aws_region_name: us-east-1 ``` **2. Start the proxy** ```bash docker run -d \\ -p 4000:4000 \\ -e AWS_ACCESS_KEY_ID=$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-v1.83.3-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" } ] }' ``` ## 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: ```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" }' ``` 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." } ] }' ``` ### 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. ```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": "xhigh" } }' ``` **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": "xhigh"}} ) ``` **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": "xhigh"}, ) ``` You can combine `reasoning_effort` with `output_config` for even more fine-grained control over the model's behavior. ```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": "xhigh" } }' ``` **Effort level guide:** | Effort | When to use | |--------|-------------| | `low` | Short, fast responses โ€” simple lookups, formatting, classification | | `medium` | Balanced tradeoff for everyday Q&A and light reasoning | | `high` (default) | Complex reasoning, code generation, analysis | | `xhigh` | Hardest problems โ€” multi-step math, deep research, agentic planning | --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/index-52) ยท https://claudary.paisolsolutions.com