Anthropic

LiteLLM supports all anthropic models.

• claude-opus-4-6 (claude-opus-4-6-20260205)
• claude-sonnet-4-6
• claude-sonnet-4-5-20250929
• claude-opus-4-5-20251101
• claude-opus-4-1-20250805
• claude-4 (claude-opus-4-20250514, claude-sonnet-4-20250514)
• claude-3.7 (claude-3-7-sonnet-20250219)
• claude-3.5 (claude-3-5-sonnet-20240620)
• claude-3 (claude-3-haiku-20240307, claude-3-opus-20240229, claude-3-sonnet-20240229)
• claude-2
• claude-2.1
• claude-instant-1.2

Supported OpenAI Parameters

Check this in code, here

"stream",
"stop",
"temperature",
"top_p",
"max_tokens",
"max_completion_tokens",
"tools",
"tool_choice",
"extra_headers",
"parallel_tool_calls",
"response_format",
"user",
"reasoning_effort",

:::info

Notes:

• Anthropic API fails requests when max_tokens are not passed. Due to this litellm passes max_tokens=4096 when no max_tokens are passed.
• response_format is fully supported for Claude Sonnet 4.5 and Opus 4.1 models (see Structured Outputs section)
• reasoning_effort is automatically mapped to output_config={"effort": ...} for Claude 4.6 and Opus 4.5 models (see Effort Parameter)

:::

Structured Outputs

LiteLLM supports Anthropic's structured outputs feature for Claude Sonnet 4.5 and Opus 4.1 models. When you use response_format with these models, LiteLLM automatically:

• Adds the required structured-outputs-2025-11-13 beta header
• Transforms OpenAI's response_format to Anthropic's output_format format

Supported Models

• sonnet-4-5 or sonnet-4.5 (all Sonnet 4.5 variants)
• opus-4-1 or opus-4.1 (all Opus 4.1 variants)
  • opus-4-5 or opus-4.5 (all Opus 4.5 variants)

Example Usage

from litellm import completion

response = completion(
    model="claude-sonnet-4-5-20250929",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "capital_response",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {
                    "country": {"type": "string"},
                    "capital": {"type": "string"}
                },
                "required": ["country", "capital"],
                "additionalProperties": False
            }
        }
    }
)

print(response.choices[0].message.content)
# Output: {"country": "France", "capital": "Paris"}

1. Setup config.yaml

model_list:
  - model_name: claude-sonnet-4-5
    litellm_params:
      model: anthropic/claude-sonnet-4-5-20250929
      api_key: os.environ/ANTHROPIC_API_KEY

2. Start proxy

litellm --config /path/to/config.yaml

3. Test it!

curl http://0.0.0.0:4000/v1/chat/completions \\
  -H "Content-Type: application/json" \\
  -H "Authorization: Bearer $LITELLM_KEY" \\
  -d '{
    "model": "claude-sonnet-4-5",
    "messages": [{"role": "user", "content": "What is the capital of France?"}],
    "response_format": {
        "type": "json_schema",
        "json_schema": {
            "name": "capital_response",
            "strict": true,
            "schema": {
                "type": "object",
                "properties": {
                    "country": {"type": "string"},
                    "capital": {"type": "string"}
                },
                "required": ["country", "capital"],
                "additionalProperties": false
            }
        }
    }
  }'

:::info When using structured outputs with supported models, LiteLLM automatically:

• Converts OpenAI's response_format to Anthropic's output_schema
• Adds the anthropic-beta: structured-outputs-2025-11-13 header
• Creates a tool with the schema and forces the model to use it :::

API Keys

os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
# os.environ["ANTHROPIC_API_BASE"] = "" # [OPTIONAL] or 'ANTHROPIC_BASE_URL'
# os.environ["LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX"] = "true" # [OPTIONAL] Disable automatic URL suffix appending

:::tip Azure Foundry Support

Claude models are also available via Microsoft Azure Foundry. Use the azure/ prefix instead of anthropic/ and configure Azure authentication. See the Azure Anthropic documentation for details.

Example:

response = completion(
    model="azure/claude-sonnet-4-5",
    api_base="https://<resource-name>.services.ai.azure.com/anthropic",
    api_key="your-azure-api-key",
    messages=[{"role": "user", "content": "Hello!"}]
)

:::

Custom API Base

When using a custom API base for Anthropic (e.g., a proxy or custom endpoint), LiteLLM automatically appends the appropriate suffix (/v1/messages or /v1/complete) to your base URL.

If your custom endpoint already includes the full path or doesn't follow Anthropic's standard URL structure, you can disable this automatic suffix appending:

os.environ["ANTHROPIC_API_BASE"] = "https://my-custom-endpoint.com/custom/path"
os.environ["LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX"] = "true"  # Prevents automatic suffix

Without LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX:

• Base URL https://my-proxy.com → https://my-proxy.com/v1/messages
• Base URL https://my-proxy.com/api → https://my-proxy.com/api/v1/messages

With LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX=true:

• Base URL https://my-proxy.com/custom/path → https://my-proxy.com/custom/path (unchanged)

Azure AI Foundry (Alternative Method)

:::tip Recommended Method For full Azure support including Azure AD authentication, use the dedicated Azure Anthropic provider with azure_ai/ prefix. :::

As an alternative, you can use the anthropic/ provider directly with your Azure endpoint since Azure exposes Claude using Anthropic's native API.

from litellm import completion

response = completion(
    model="anthropic/claude-sonnet-4-5",
    api_base="https://<your-resource>.services.ai.azure.com/anthropic",
    api_key="<your-azure-api-key>",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response)

:::info Finding your Azure endpoint: Go to Azure AI Foundry → Your deployment → Overview. Your base URL will be https://<resource-name>.services.ai.azure.com/anthropic :::

Usage

from litellm import completion

# set env - [OPTIONAL] replace with your anthropic key
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"

messages = [{"role": "user", "content": "Hey! how's it going?"}]
response = completion(model="claude-opus-4-20250514", messages=messages)
print(response)

Usage - Streaming

Just set stream=True when calling completion.

from litellm import completion

# set env
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"

messages = [{"role": "user", "content": "Hey! how's it going?"}]
response = completion(model="claude-opus-4-20250514", messages=messages, stream=True)
for chunk in response:
    print(chunk["choices"][0]["delta"]["content"])  # same as openai format

Usage with LiteLLM Proxy

Here's how to call Anthropic with the LiteLLM Proxy Server

1. Save key in your environment

2. Start the proxy

model_list:
  - model_name: claude-4 ### RECEIVED MODEL NAME ###
    litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input
      model: claude-opus-4-20250514 ### MODEL NAME sent to `litellm.completion()` ###
      api_key: "os.environ/ANTHROPIC_API_KEY" # does os.getenv("ANTHROPIC_API_KEY")

litellm --config /path/to/config.yaml

Use this if you want to make requests to claude-3-haiku-20240307,claude-3-opus-20240229,claude-2.1 without defining them on the config.yaml

Required env variables

ANTHROPIC_API_KEY=sk-ant****

model_list:
  - model_name: "*" 
    litellm_params:
      model: "*"

litellm --config /path/to/config.yaml

Example Request for this config.yaml

Ensure you use anthropic/ prefix to route the request to Anthropic API

curl --location 'http://0.0.0.0:4000/chat/completions' \\
--header 'Content-Type: application/json' \\
--data ' {
      "model": "anthropic/claude-3-haiku-20240307",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ]
    }
'

$ litellm --model claude-opus-4-20250514

# Server running on http://0.0.0.0:4000

3. Test it

curl --location 'http://0.0.0.0:4000/chat/completions' \\
--header 'Content-Type: application/json' \\
--data ' {
      "model": "claude-3",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ]
    }
'

client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="claude-3", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
])

print(response)

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage

chat = ChatOpenAI(
    openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
    model = "claude-3",
    temperature=0.1
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that im using to make a test request to."
    ),
    HumanMessage(
        content="test from litellm. tell me why it's amazing in 1 sentence"
    ),
]
response = chat(messages)

print(response)

Supported Models

Model Name 👉 Human-friendly name.
Function Call 👉 How to call the model in LiteLLM.

Prompt Caching

Use Anthropic Prompt Caching

Relevant Anthropic API Docs

:::note

Here's what a sample Raw Request from LiteLLM for Anthropic Context Caching looks like:

POST Request Sent from LiteLLM:
curl -X POST \\
https://api.anthropic.com/v1/messages \\
-H 'accept: application/json' -H 'anthropic-version: 2023-06-01' -H 'content-type: application/json' -H 'x-api-key: sk-...' \\
-d '{'model': 'claude-3-5-sonnet-20240620', [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What are the key terms and conditions in this agreement?",
          "cache_control": {
            "type": "ephemeral"
          }
        }
      ]
    },
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "Certainly! The key terms and conditions are the following: the contract is 1 year long for $10/mo"
        }
      ]
    }
  ],
  "temperature": 0.2,
  "max_tokens": 10
}'

Note: Anthropic no longer requires the anthropic-beta: prompt-caching-2024-07-31 header. Prompt caching now works automatically when you use cache_control in your message