Day 0 Support: Claude Opus 5

LiteLLM now supports Claude Opus 5 on Day 0. Use it across Anthropic, Azure, Vertex AI, and Bedrock through the LiteLLM AI Gateway. Call it with the same OpenAI-compatible request you already use, and track spend, rate limits, and logging in one place.

What's new in Opus 5

Key changes (details from Anthropic):

• Much better coding agents: stronger at large refactors, debugging, multi-file features, and finishing end-to-end work without leaving stubs
• Better self-verification: it checks and iterates on its own work more proactively
• Better vision and artifacts: improved screenshots → website, charts and documents, spreadsheets, and slide decks
• Same base API price as Opus 4.8: $5 / MTok input and $25 / MTok output
• Thinking is on by default
• Fast mode: about 2.5x faster, but costs 2x

Before you switch from Opus 4.8

The swap is not free. Priority Tier and web fetch are both gone on Opus 5, so plan that capacity and that tooling separately. Opus 5's cybersecurity classifiers can also decline a request outright with stop_reason: "refusal" and a category in stop_details, which is worth handling explicitly or routing around with LiteLLM fallbacks.

Sampling parameters (temperature, top_p, top_k), fixed thinking budgets, and assistant message prefill remain unsupported, same as Opus 4.8. Anthropic's Opus 5 migration guide has the full compatibility checklist.

Enabling Opus 5

Opus 5 ships in the v1.95.0-dev.3 image, cutting later today, but most proxies do not need to upgrade at all. On the default remote cost map, open the Price Data tab under Models + Endpoints in the UI and click Reload Price Data (or POST /reload/model_cost_map as a proxy admin). That refetches pricing and re-registers provider routing in one step, so claude-opus-5 becomes available across Anthropic, Azure, Vertex AI, and Bedrock even on an older version.

The exception is LITELLM_LOCAL_MODEL_COST_MAP=true, which bakes the cost map into the image and puts it out of the Reload button's reach. Pull v1.95.0-dev.3 or later for the bundled Opus 5 metadata:

docker pull ghcr.io/berriai/litellm:v1.95.0-dev.3

Usage

Pick your provider below. Each tab wires up claude-opus-5 for that provider; the request you send afterward is identical everywhere.

Anthropic

1. Setup config.yaml

model_list:
  - model_name: claude-opus-5
    litellm_params:
      model: anthropic/claude-opus-5
      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:v1.95.0-dev.3 \
  --config /app/config.yaml

Azure

1. Setup config.yaml

model_list:
  - model_name: claude-opus-5
    litellm_params:
      model: azure_ai/claude-opus-5
      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:v1.95.0-dev.3 \
  --config /app/config.yaml

Vertex AI

1. Setup config.yaml

model_list:
  - model_name: claude-opus-5
    litellm_params:
      model: vertex_ai/claude-opus-5
      vertex_project: os.environ/VERTEX_PROJECT
      vertex_location: global

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:v1.95.0-dev.3 \
  --config /app/config.yaml

Bedrock

1. Setup config.yaml

model_list:
  - model_name: claude-opus-5
    litellm_params:
      model: bedrock/anthropic.claude-opus-5
      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

note

For cross-region routing, swap the model ID for a regional inference profile (us., eu., au., or jp. prefix), e.g. bedrock/converse/us.anthropic.claude-opus-5. These carry a 10% regional premium; the global. profile stays at base price. LiteLLM tracks the cost of each variant automatically.

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:v1.95.0-dev.3 \
  --config /app/config.yaml

3. Test it!

The request is the same regardless of which provider you configured above:

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "claude-opus-5",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

Fast Mode

info

Fast mode is only supported on the Anthropic provider (anthropic/claude-opus-5). It is not available on Azure AI, Vertex AI, or Bedrock, and it cannot be combined with the Batch API.

Opus 5 runs roughly 2.5x faster with speed: "fast", billed at $10 / MTok input and $50 / MTok output (2x the standard rate, down from the 6x premium on Opus 4.6). LiteLLM adds the fast-mode-2026-02-01 beta header and tracks the premium in cost calculations automatically.

/chat/completions

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "claude-opus-5",
  "messages": [
    {
      "role": "user",
      "content": "Refactor this module..."
    }
  ],
  "max_tokens": 4096,
  "speed": "fast"
}'

/v1/messages

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-5",
    "max_tokens": 4096,
    "speed": "fast",
    "messages": [
        {
            "role": "user",
            "content": "Refactor this module..."
        }
    ]
}'

Feedback

Running Opus 5 through LiteLLM and hitting something unexpected? Share it on GitHub discussion #34517.