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        POST
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  1. Example: Anthropic

Messages

POST
/v1/messages
Send a structured list of input messages with text and/or image content, and the model will generate the next message in the conversation.
The Messages API can be used for either single queries or stateless multi-turn conversations.
Request Request Example
Shell
JavaScript
Java
Swift
curl --location --request POST '/v1/messages' \
--header 'anthropic-beta;' \
--header 'anthropic-version: 2023-06-01' \
--header 'x-api-key: $ANTHROPIC_API_KEY' \
--header 'Content-Type: application/json' \
--data-raw '{
    "model": "claude-3-7-sonnet-20250219",
    "max_tokens": 1024,
    "messages": [
        {
            "role": "user",
            "content": "Hello, world"
        }
    ]
}'
Response Response Example
200 - Example 1
{
    "content": [
        {
            "text": "Hi! My name is Claude.",
            "type": "text"
        }
    ],
    "id": "msg_013Zva2CMHLNnXjNJJKqJ2EF",
    "model": "claude-3-7-sonnet-20250219",
    "role": "assistant",
    "stop_reason": "end_turn",
    "stop_sequence": null,
    "type": "message",
    "usage": {
        "input_tokens": 2095,
        "output_tokens": 503
    }
}

Request

Header Params
anthropic-beta
string 
optional
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 Anthropic API you want to use.
Example:
2023-06-01
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.
Example:
$ANTHROPIC_API_KEY
Body Params application/json
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.
> 1
messages
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"}]}
Starting with Claude 3 models, you can also send image content blocks:
{"role": "user", "content": [
  {
    "type": "image",
    "source": {
      "type": "base64",
      "media_type": "image/jpeg",
      "data": "/9j/4AAQSkZJRg...",
    }
  },
  {"type": "text", "text": "What is in this image?"}
]}
We currently support the base64 source type for images, and the image/jpeg, image/png, image/gif, and image/webp media types.
See examples for more 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.
content
string 
required
role
enum<string> 
required
Allowed values:
userassistant
model
string 
required
The model that will complete your prompt.
>= 1 characters<= 256 characters
metadata
object 
required
An object describing metadata about the request.
user_id
string  | null 
required
An external identifier for the user who is associated with the request.
This should be a uuid, hash value, or other opaque identifier. Anthropic may use this id to help detect abuse. Do not include any identifying information such as name, email address, or phone number.
<= 256 characters
stop_sequences
string 
required
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 
required
Whether to incrementally stream the response using server-sent events.
system
string 
required
System prompt.
A system prompt is a way of providing context and instructions to Claude, such as specifying a particular goal or role.
temperature
number 
required
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.
> 0< 1
thinking
required
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.
One of
Enabled
budget_tokens
integer 
required
Determines how many tokens Claude can use for its internal reasoning process. Larger budgets can enable more thorough analysis for complex problems, improving response quality.
Must be ≥1024 and less than max_tokens.
> 1024
type
enum<string> 
required
Allowed value:
enabled
tool_choice
required
One of
Auto
type
enum<string> 
required
Allowed value:
auto
disable_parallel_tool_use
boolean 
required
Whether to disable parallel tool use.
Defaults to false. If set to true, the model will output at most one tool use.
tools
object 
required
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.
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.
top_k
integer 
required
Only sample from the top K options for each subsequent token.
Used to remove "long tail" low probability responses.
Recommended for advanced use cases only. You usually only need to use temperature.
> 0
top_p
number 
required
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.
> 0< 1
Examples

Responses

🟢200Success
application/json
Body
content
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)"}]
One of
Text
citations
object  | null 
required
Citations supporting the text block.
The type of citation returned will depend on the type of document being cited. Citing a PDF results in page_location, plain text results in char_location, and content document results in content_block_location.
text
string 
required
<= 5000000 characters
type
enum<string> 
required
Allowed value:
text
Default:
text
id
string 
required
Unique object identifier.
The format and length of IDs may change over time.
model
string 
required
The model that handled the request.
>= 1 characters<= 256 characters
role
enum<string> 
required
Conversational role of the generated message.
This will always be "assistant".
Allowed value:
assistant
Default:
assistant
stop_reason
enum<string> 
required
The reason that we stopped.
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.
Allowed values:
end_turnmax_tokensstop_sequencetool_use
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.
type
enum<string> 
required
Object type.
For Messages, this is always "message".
Allowed value:
message
Default:
message
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.
cache_creation_input_tokens
integer  | null 
required
The number of input tokens used to create the cache entry.
> 0
cache_read_input_tokens
integer  | null 
required
The number of input tokens read from the cache.
> 0
input_tokens
integer 
required
The number of input tokens which were used.
> 0
output_tokens
integer 
required
> 0
🟠400Bad Request
Modified at 2025-03-25 05:47:01
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