📄 claude/api/messages

File: messages.md | Updated: 11/15/2025

Source: https://docs.claude.com/en/api/messages

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Home Developer Guide API Reference Model Context Protocol (MCP) Resources Release Notes

cURL

cURL

Copy

curl https://api.anthropic.com/v1/messages \
     --header "x-api-key: $ANTHROPIC_API_KEY" \
     --header "anthropic-version: 2023-06-01" \
     --header "content-type: application/json" \
     --data \
'{
    "model": "claude-sonnet-4-5-20250929",
    "max_tokens": 1024,
    "messages": [\
        {"role": "user", "content": "Hello, world"}\
    ]
}'

200

4XX

Copy

{
  "content": [\
    {\
      "citations": null,\
      "text": "Hi! My name is Claude.",\
      "type": "text"\
    }\
  ],
  "id": "msg_013Zva2CMHLNnXjNJJKqJ2EF",
  "model": "claude-sonnet-4-5-20250929",
  "role": "assistant",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "type": "message",
  "usage": {
    "input_tokens": 2095,
    "output_tokens": 503
  }
}

POST

/

v1

/

messages

cURL

cURL

Copy

curl https://api.anthropic.com/v1/messages \
     --header "x-api-key: $ANTHROPIC_API_KEY" \
     --header "anthropic-version: 2023-06-01" \
     --header "content-type: application/json" \
     --data \
'{
    "model": "claude-sonnet-4-5-20250929",
    "max_tokens": 1024,
    "messages": [\
        {"role": "user", "content": "Hello, world"}\
    ]
}'

200

4XX

Copy

{
  "content": [\
    {\
      "citations": null,\
      "text": "Hi! My name is Claude.",\
      "type": "text"\
    }\
  ],
  "id": "msg_013Zva2CMHLNnXjNJJKqJ2EF",
  "model": "claude-sonnet-4-5-20250929",
  "role": "assistant",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "type": "message",
  "usage": {
    "input_tokens": 2095,
    "output_tokens": 503
  }
}

Headers

​

anthropic-beta

string[]

Optional header to specify the beta version(s) you want to use.

To use multiple betas, use a comma separated list like beta1,beta2 or specify the header multiple times for each beta.

​

anthropic-version

string

required

The version of the Claude API you want to use.

Read more about versioning and our version history here .

​

x-api-key

string

required

Your unique API key for authentication.

This key is required in the header of all API requests, to authenticate your account and access Anthropic's services. Get your API key through the Console . Each key is scoped to a Workspace.

Body

application/json

​

model

string

required

The model that will complete your prompt.

See models for additional details and options.

Required string length: 1 - 256

Examples:

"claude-sonnet-4-5-20250929"

​

messages

InputMessage · object[]

required

Input messages.

Our models are trained to operate on alternating user and assistant conversational turns. When creating a new Message, you specify the prior conversational turns with the messages parameter, and the model then generates the next Message in the conversation. Consecutive user or assistant turns in your request will be combined into a single turn.

Each input message must be an object with a role and content. You can specify a single user-role message, or you can include multiple user and assistant messages.

If the final message uses the assistant role, the response content will continue immediately from the content in that message. This can be used to constrain part of the model's response.

Example with a single user message:

[{"role": "user", "content": "Hello, Claude"}]

Example with multiple conversational turns:

[  {"role": "user", "content": "Hello there."},  {"role": "assistant", "content": "Hi, I'm Claude. How can I help you?"},  {"role": "user", "content": "Can you explain LLMs in plain English?"},]

Example with a partially-filled response from Claude:

[  {"role": "user", "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"},  {"role": "assistant", "content": "The best answer is ("},]

Each input message content may be either a single string or an array of content blocks, where each block has a specific type. Using a string for content is shorthand for an array of one content block of type "text". The following input messages are equivalent:

{"role": "user", "content": "Hello, Claude"}

{"role": "user", "content": [{"type": "text", "text": "Hello, Claude"}]}

See input examples .

Note that if you want to include a system prompt , you can use the top-level system parameter — there is no "system" role for input messages in the Messages API.

There is a limit of 100,000 messages in a single request.

Show child attributes

​

max_tokens

integer

required

The maximum number of tokens to generate before stopping.

Note that our models may stop before reaching this maximum. This parameter only specifies the absolute maximum number of tokens to generate.

Different models have different maximum values for this parameter. See models for details.

Required range: x >= 1

Examples:

1024

​

container

ContainerParams · objectstring | null

Container identifier for reuse across requests. Container parameters with skills to be loaded.

Show child attributes

​

context_management

object | null

Context management configuration.

This allows you to control how Claude manages context across multiple requests, such as whether to clear function results or not.

Show child attributes

​

mcp_servers

RequestMCPServerURLDefinition · object[]

MCP servers to be utilized in this request

Maximum length: 20

Show child attributes

​

metadata

object

An object describing metadata about the request.

Show child attributes

​

service_tier

enum<string>

Determines whether to use priority capacity (if available) or standard capacity for this request.

Anthropic offers different levels of service for your API requests. See service-tiers for details.

Available options:

auto,

standard_only

​

stop_sequences

string[]

Custom text sequences that will cause the model to stop generating.

Our models will normally stop when they have naturally completed their turn, which will result in a response stop_reason of "end_turn".

If you want the model to stop generating when it encounters custom strings of text, you can use the stop_sequences parameter. If the model encounters one of the custom sequences, the response stop_reason value will be "stop_sequence" and the response stop_sequence value will contain the matched stop sequence.

​

stream

boolean

Whether to incrementally stream the response using server-sent events.

See streaming for details.

​

system

stringText · object[]

System prompt.

A system prompt is a way of providing context and instructions to Claude, such as specifying a particular goal or role. See our guide to system prompts .

Examples:

[  {    "text": "Today's date is 2024-06-01.",    "type": "text"  }]

"Today's date is 2023-01-01."

​

temperature

number

Amount of randomness injected into the response.

Defaults to 1.0. Ranges from 0.0 to 1.0. Use temperature closer to 0.0 for analytical / multiple choice, and closer to 1.0 for creative and generative tasks.

Note that even with temperature of 0.0, the results will not be fully deterministic.

Required range: 0 <= x <= 1

Examples:

1

​

thinking

object

Configuration for enabling Claude's extended thinking.

When enabled, responses include thinking content blocks showing Claude's thinking process before the final answer. Requires a minimum budget of 1,024 tokens and counts towards your max_tokens limit.

See extended thinking for details.

  • Enabled
  • Disabled

Show child attributes

​

tool_choice

object

How the model should use the provided tools. The model can use a specific tool, any available tool, decide by itself, or not use tools at all. The model will automatically decide whether to use tools.

  • Auto
  • Any
  • Tool
  • None

Show child attributes

​

tools

Tools · array

Definitions of tools that the model may use.

If you include tools in your API request, the model may return tool_use content blocks that represent the model's use of those tools. You can then run those tools using the tool input generated by the model and then optionally return results back to the model using tool_result content blocks.

There are two types of tools: client tools and server tools. The behavior described below applies to client tools. For server tools , see their individual documentation as each has its own behavior (e.g., the web search tool ).

Each tool definition includes:

  • name: Name of the tool.
  • description: Optional, but strongly-recommended description of the tool.
  • input_schema: JSON schema for the tool input shape that the model will produce in tool_use output content blocks.

For example, if you defined tools as:

[  {    "name": "get_stock_price",    "description": "Get the current stock price for a given ticker symbol.",    "input_schema": {      "type": "object",      "properties": {        "ticker": {          "type": "string",          "description": "The stock ticker symbol, e.g. AAPL for Apple Inc."        }      },      "required": ["ticker"]    }  }]

And then asked the model "What's the S&P 500 at today?", the model might produce tool_use content blocks in the response like this:

[  {    "type": "tool_use",    "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV",    "name": "get_stock_price",    "input": { "ticker": "^GSPC" }  }]

You might then run your get_stock_price tool with {"ticker": "^GSPC"} as an input, and return the following back to the model in a subsequent user message:

[  {    "type": "tool_result",    "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV",    "content": "259.75 USD"  }]

Tools can be used for workflows that include running client-side tools and functions, or more generally whenever you want the model to produce a particular JSON structure of output.

See our guide for more details.

  • Custom tool
  • Bash tool (2024-10-22)
  • Bash tool (2025-01-24)
  • Code execution tool (2025-05-22)
  • CodeExecutionTool_20250825
  • Computer use tool (2024-01-22)
  • MemoryTool_20250818
  • Computer use tool (2025-01-24)
  • Text editor tool (2024-10-22)
  • Text editor tool (2025-01-24)
  • Text editor tool (2025-04-29)
  • TextEditor_20250728
  • Web search tool (2025-03-05)
  • WebFetchTool_20250910

Show child attributes

Examples:

{  "description": "Get the current weather in a given location",  "input_schema": {    "properties": {      "location": {        "description": "The city and state, e.g. San Francisco, CA",        "type": "string"      },      "unit": {        "description": "Unit for the output - one of (celsius, fahrenheit)",        "type": "string"      }    },    "required": ["location"],    "type": "object"  },  "name": "get_weather"}

​

top_k

integer

Only sample from the top K options for each subsequent token.

Used to remove "long tail" low probability responses. Learn more technical details here .

Recommended for advanced use cases only. You usually only need to use temperature.

Required range: x >= 0

Examples:

5

​

top_p

number

Use nucleus sampling.

In nucleus sampling, we compute the cumulative distribution over all the options for each subsequent token in decreasing probability order and cut it off once it reaches a particular probability specified by top_p. You should either alter temperature or top_p, but not both.

Recommended for advanced use cases only. You usually only need to use temperature.

Required range: 0 <= x <= 1

Examples:

0.7

Response

200

application/json

Message object.

​

id

string

required

Unique object identifier.

The format and length of IDs may change over time.

Examples:

"msg_013Zva2CMHLNnXjNJJKqJ2EF"

​

type

string

default:message

required

Object type.

For Messages, this is always "message".

Allowed value: "message"

​

role

string

default:assistant

required

Conversational role of the generated message.

This will always be "assistant".

Allowed value: "assistant"

​

content

Content · array

required

Content generated by the model.

This is an array of content blocks, each of which has a type that determines its shape.

Example:

[{"type": "text", "text": "Hi, I'm Claude."}]

If the request input messages ended with an assistant turn, then the response content will continue directly from that last turn. You can use this to constrain the model's output.

For example, if the input messages were:

[  {"role": "user", "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"},  {"role": "assistant", "content": "The best answer is ("}]

Then the response content might be:

[{"type": "text", "text": "B)"}]
  • Text
  • Thinking
  • Redacted thinking
  • Tool use
  • Server tool use
  • Web search tool result
  • ResponseWebFetchToolResultBlock
  • Code execution tool result
  • ResponseBashCodeExecutionToolResultBlock
  • ResponseTextEditorCodeExecutionToolResultBlock
  • MCP tool use
  • MCP tool result
  • Container upload

Show child attributes

Examples:

[  {    "citations": null,    "text": "Hi! My name is Claude.",    "type": "text"  }]

​

model

string

required

The model that handled the request.

Required string length: 1 - 256

Examples:

"claude-sonnet-4-5-20250929"

​

stop_reason

enum<string> | null

required

The reason that we stopped.

This may be one the following values:

  • "end_turn": the model reached a natural stopping point
  • "max_tokens": we exceeded the requested max_tokens or the model's maximum
  • "stop_sequence": one of your provided custom stop_sequences was generated
  • "tool_use": the model invoked one or more tools
  • "pause_turn": we paused a long-running turn. You may provide the response back as-is in a subsequent request to let the model continue.
  • "refusal": when streaming classifiers intervene to handle potential policy violations

In non-streaming mode this value is always non-null. In streaming mode, it is null in the message_start event and non-null otherwise.

Available options:

end_turn,

max_tokens,

stop_sequence,

tool_use,

pause_turn,

refusal,

model_context_window_exceeded

​

stop_sequence

string | null

required

Which custom stop sequence was generated, if any.

This value will be a non-null string if one of your custom stop sequences was generated.

​

usage

object

required

Billing and rate-limit usage.

Anthropic's API bills and rate-limits by token counts, as tokens represent the underlying cost to our systems.

Under the hood, the API transforms requests into a format suitable for the model. The model's output then goes through a parsing stage before becoming an API response. As a result, the token counts in usage will not match one-to-one with the exact visible content of an API request or response.

For example, output_tokens will be non-zero, even for an empty string response from Claude.

Total input tokens in a request is the summation of input_tokens, cache_creation_input_tokens, and cache_read_input_tokens.

Show child attributes

​

context_management

object | null

required

Context management response.

Information about context management strategies applied during the request.

Show child attributes

​

container

object | null

required

Information about the container used in this request.

This will be non-null if a container tool (e.g. code execution) was used. Information about the container used in the request (for the code execution tool)

Show child attributes

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Errors Count Message tokens

Assistant

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