Day 0 Support: Claude Opus 4.8

May 28, 2026

Mateo Wang

AI Engineer, LiteLLM

Krrish Dholakia

CEO, LiteLLM

Ishaan Jaffer

CTO, LiteLLM

LiteLLM now supports Claude Opus 4.8 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 4.8

Opus 4.8 builds on Opus 4.7 with gains across coding, agentic, and reasoning benchmarks, and ships at the same price. A few things stand out for teams running it through a gateway:

A sharper, more honest agent. Anthropic reports Opus 4.8 is roughly 4× less likely than Opus 4.7 to let flaws in code it wrote pass unremarked, and more likely to flag uncertainty than make unsupported claims. That reliability compounds when the model is driving multi-step tool calls behind your proxy. (details from Anthropic)
The full effort ladder, per request. low, medium, high (default), xhigh, and max. Dial reasoning up for hard, long-running agentic work or down for fast, cheap responses. Set it per call via reasoning_effort or output_config.
Mid-task system messages. The Messages API now accepts system entries inside the messages array, so an agent can update its instructions, permissions, or token budget mid-run without breaking the prompt cache, and it flows straight through LiteLLM's /v1/messages passthrough.
Same per-token price as Opus 4.7. $5 / MTok input and $25 / MTok output, with prompt caching at $0.50 / MTok (read) and $6.25 / MTok (write). Better results, no price change.
1M-token context, up to 128K output tokens.
One gateway, every surface. Vision, PDF input, computer use, tool calling, prompt caching, adaptive thinking, and structured output, all available across Anthropic, Azure, Vertex AI, and Bedrock with unified spend tracking, logging, and fallbacks.

Enabling Opus 4.8

Opus 4.8 ships in the nightly v1.88.0-dev.1 image (and every release after it). How you pick it up depends on where your proxy reads pricing from:

Default (remote cost map): no upgrade needed. In the LiteLLM UI, open the Price Data tab under Models + Endpoints and click Reload Price Data (or, as a proxy admin, POST /reload/model_cost_map). This refetches the latest pricing from LiteLLM's cost map and re-registers provider routing in one step, so claude-opus-4-8 becomes available across Anthropic, Azure, Vertex AI, and Bedrock, even if you're on an older proxy version.
Running LITELLM_LOCAL_MODEL_COST_MAP=true? The cost map is baked into the image, so the Reload button won't reach it. Pull v1.88.0-dev.1 or later to get the bundled Opus 4.8 metadata:
```
docker pull ghcr.io/berriai/litellm:v1.88.0-dev.1
```

Usage - Anthropic

LiteLLM Proxy

1. Setup config.yaml

model_list:
  - model_name: claude-opus-4-8
    litellm_params:
      model: anthropic/claude-opus-4-8
      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:v1.88.0-dev.1 \
  --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-4-8",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

Usage - Azure

LiteLLM Proxy

1. Setup config.yaml

model_list:
  - model_name: claude-opus-4-8
    litellm_params:
      model: azure_ai/claude-opus-4-8
      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:v1.88.0-dev.1 \
  --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-4-8",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

Usage - Vertex AI

LiteLLM Proxy

1. Setup config.yaml

model_list:
  - model_name: claude-opus-4-8
    litellm_params:
      model: vertex_ai/claude-opus-4-8
      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:v1.88.0-dev.1 \
  --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-4-8",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

Usage - Bedrock

LiteLLM Proxy

1. Setup config.yaml

model_list:
  - model_name: claude-opus-4-8
    litellm_params:
      model: bedrock/anthropic.claude-opus-4-8
      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:v1.88.0-dev.1 \
  --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-4-8",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

Advanced Features

Adaptive Thinking

note

When using reasoning_effort with Claude Opus 4.8, 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.

/chat/completions
/v1/messages

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-4-8",
  "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-4-8",
    "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 4.8 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.

Opus 4.8 supports the full effort ladder. Both xhigh (introduced with Opus 4.7) and max (previously Opus 4.6 only) are available.

/chat/completions
/v1/messages

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "claude-opus-4-8",
  "messages": [
    {
      "role": "user",
      "content": "Explain quantum computing"
    }
  ],
  "output_config": {
    "effort": "max"
  }
}'

Using OpenAI SDK:

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-4-8",
    messages=[{"role": "user", "content": "Explain quantum computing"}],
    extra_body={"output_config": {"effort": "max"}}
)

Using LiteLLM SDK:

from litellm import completion

response = completion(
    model="anthropic/claude-opus-4-8",
    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.

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-4-8",
    "max_tokens": 4096,
    "messages": [
        {
            "role": "user",
            "content": "Explain quantum computing"
        }
    ],
    "output_config": {
        "effort": "max"
    }
}'

Effort level guide:

Effort	When to use
`low`	Short, fast responses for simple lookups, formatting, and classification
`medium`	Balanced tradeoff for everyday Q&A and light reasoning
`high` (default)	Complex reasoning, code generation, analysis
`xhigh`	Hard problems like multi-step math, deep research, and agentic planning
`max`	The hardest tasks where you want maximum reasoning depth regardless of latency

What's new in Opus 4.8​

Enabling Opus 4.8​

Usage - Anthropic​

Usage - Azure​

Usage - Vertex AI​

Usage - Bedrock​

Advanced Features​

Adaptive Thinking​

Effort Levels​

What's new in Opus 4.8

Enabling Opus 4.8

Usage - Anthropic

Usage - Azure

Usage - Vertex AI

Usage - Bedrock

Advanced Features

Adaptive Thinking

Effort Levels