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Claude Messages API

Claude Messages API reference for /v1/messages: multi-turn chat, system prompts, tool use, vision and streaming, with parameters and code examples.

  • Fully compatible with the Claude Messages API format
  • Supports multi-turn conversations and single-shot queries
  • Supports multimodal content such as text and images
curl https://anyrouter.win/v1/messages \
  -H "x-api-key: $API_KEY" \
  -H "anthropic-version: 2025-10-01" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Hello, world"}
    ]
  }'
import anthropic

client = anthropic.Anthropic(
    api_key="YOUR_API_KEY",
    base_url="https://anyrouter.win"
)

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, world"}
    ]
)

print(message.content)
import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: process.env.API_KEY,
  baseURL: "https://anyrouter.win",
});

const message = await client.messages.create({
  model: "claude-sonnet-4-5-20250929",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Hello, world" }],
});

console.log(message.content);
package main

import (
    "bytes"
    "encoding/json"
    "fmt"
    "io/ioutil"
    "net/http"
    "os"
)

func main() {
    url := "https://anyrouter.win/v1/messages"

    payload := map[string]interface{}{
        "model": "claude-sonnet-4-5-20250929",
        "max_tokens": 1024,
        "messages": []map[string]string{
            {
                "role":    "user",
                "content": "Hello, world",
            },
        },
    }

    jsonData, _ := json.Marshal(payload)

    req, _ := http.NewRequest("POST", url, bytes.NewBuffer(jsonData))
    req.Header.Set("x-api-key", os.Getenv("API_KEY"))
    req.Header.Set("anthropic-version", "2025-10-01")
    req.Header.Set("Content-Type", "application/json")

    client := &http.Client{}
    resp, err := client.Do(req)
    if err != nil {
        panic(err)
    }
    defer resp.Body.Close()

    body, _ := ioutil.ReadAll(resp.Body)
    fmt.Println(string(body))
}
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.net.URI;

public class Main {
    public static void main(String[] args) throws Exception {
        String url = "https://anyrouter.win/v1/messages";
        String apiKey = System.getenv("API_KEY");

        String payload = """
        {
          "model": "claude-sonnet-4-5-20250929",
          "max_tokens": 1024,
          "messages": [
            {
              "role": "user",
              "content": "Hello, world"
            }
          ]
        }
        """;

        HttpClient client = HttpClient.newHttpClient();
        HttpRequest request = HttpRequest.newBuilder()
            .uri(URI.create(url))
            .header("x-api-key", apiKey)
            .header("anthropic-version", "2025-10-01")
            .header("Content-Type", "application/json")
            .POST(HttpRequest.BodyPublishers.ofString(payload))
            .build();

        HttpResponse<String> response = client.send(request,
            HttpResponse.BodyHandlers.ofString());

        System.out.println(response.body());
    }
}
<?php

$url = "https://anyrouter.win/v1/messages";
$apiKey = getenv('API_KEY');

$payload = [
    "model" => "claude-sonnet-4-5-20250929",
    "max_tokens" => 1024,
    "messages" => [
        [
            "role" => "user",
            "content" => "Hello, world"
        ]
    ]
];

$ch = curl_init($url);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload));
curl_setopt($ch, CURLOPT_HTTPHEADER, [
    "x-api-key: " . $apiKey,
    "anthropic-version: 2025-10-01",
    "Content-Type: application/json"
]);

$response = curl_exec($ch);
curl_close($ch);

echo $response;
?>
require 'net/http'
require 'json'
require 'uri'

url = URI("https://anyrouter.win/v1/messages")
api_key = ENV['API_KEY']

payload = {
  model: "claude-sonnet-4-5-20250929",
  max_tokens: 1024,
  messages: [
    {
      role: "user",
      content: "Hello, world"
    }
  ]
}

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["x-api-key"] = api_key
request["anthropic-version"] = "2025-10-01"
request["Content-Type"] = "application/json"
request.body = payload.to_json

response = http.request(request)
puts response.body
import Foundation

let url = URL(string: "https://anyrouter.win/v1/messages")!
let apiKey = ProcessInfo.processInfo.environment["API_KEY"] ?? ""

let payload: [String: Any] = [
    "model": "claude-sonnet-4-5-20250929",
    "max_tokens": 1024,
    "messages": [
        [
            "role": "user",
            "content": "Hello, world"
        ]
    ]
]

var request = URLRequest(url: url)
request.httpMethod = "POST"
request.setValue(apiKey, forHTTPHeaderField: "x-api-key")
request.setValue("2025-10-01", forHTTPHeaderField: "anthropic-version")
request.setValue("application/json", forHTTPHeaderField: "Content-Type")
request.httpBody = try? JSONSerialization.data(withJSONObject: payload)

let task = URLSession.shared.dataTask(with: request) { data, response, error in
    if let error = error {
        print("Error: \(error)")
        return
    }

    if let data = data, let responseString = String(data: data, encoding: .utf8) {
        print(responseString)
    }
}

task.resume()
using System;
using System.Net.Http;
using System.Text;
using System.Threading.Tasks;

class Program
{
    static async Task Main(string[] args)
    {
        var url = "https://anyrouter.win/v1/messages";
        var apiKey = Environment.GetEnvironmentVariable("API_KEY");

        var payload = @"{
            ""model"": ""claude-sonnet-4-5-20250929"",
            ""max_tokens"": 1024,
            ""messages"": [
                {
                    ""role"": ""user"",
                    ""content"": ""Hello, world""
                }
            ]
        }";

        using var client = new HttpClient();
        client.DefaultRequestHeaders.Add("x-api-key", apiKey);
        client.DefaultRequestHeaders.Add("anthropic-version", "2025-10-01");

        var content = new StringContent(payload, Encoding.UTF8, "application/json");
        var response = await client.PostAsync(url, content);
        var result = await response.Content.ReadAsStringAsync();

        Console.WriteLine(result);
    }
}
#include <stdio.h>
#include <curl/curl.h>
#include <stdlib.h>

int main(void) {
    CURL *curl;
    CURLcode res;
    const char *api_key = getenv("API_KEY");

    curl_global_init(CURL_GLOBAL_DEFAULT);
    curl = curl_easy_init();

    if(curl) {
        const char *url = "https://anyrouter.win/v1/messages";
        const char *payload = "{"
            "\"model\":\"claude-sonnet-4-5-20250929\","
            "\"max_tokens\":1024,"
            "\"messages\":[{\"role\":\"user\",\"content\":\"Hello, world\"}]"
        "}";

        char auth_header[256];
        snprintf(auth_header, sizeof(auth_header), "x-api-key: %s", api_key);

        struct curl_slist *headers = NULL;
        headers = curl_slist_append(headers, auth_header);
        headers = curl_slist_append(headers, "anthropic-version: 2025-10-01");
        headers = curl_slist_append(headers, "Content-Type: application/json");

        curl_easy_setopt(curl, CURLOPT_URL, url);
        curl_easy_setopt(curl, CURLOPT_POSTFIELDS, payload);
        curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);

        res = curl_easy_perform(curl);

        if(res != CURLE_OK) {
            fprintf(stderr, "curl_easy_perform() failed: %s\n",
                    curl_easy_strerror(res));
        }

        curl_slist_free_all(headers);
        curl_easy_cleanup(curl);
    }

    curl_global_cleanup();
    return 0;
}
#import <Foundation/Foundation.h>

int main(int argc, const char * argv[]) {
    @autoreleasepool {
        NSURL *url = [NSURL URLWithString:@"https://anyrouter.win/v1/messages"];
        NSString *apiKey = [NSProcessInfo processInfo].environment[@"API_KEY"];

        NSDictionary *payload = @{
            @"model": @"claude-sonnet-4-5-20250929",
            @"max_tokens": @1024,
            @"messages": @[
                @{
                    @"role": @"user",
                    @"content": @"Hello, world"
                }
            ]
        };

        NSError *error;
        NSData *jsonData = [NSJSONSerialization dataWithJSONObject:payload
                                                          options:0
                                                            error:&error];

        NSMutableURLRequest *request = [NSMutableURLRequest requestWithURL:url];
        [request setHTTPMethod:@"POST"];
        [request setValue:apiKey forHTTPHeaderField:@"x-api-key"];
        [request setValue:@"2025-10-01" forHTTPHeaderField:@"anthropic-version"];
        [request setValue:@"application/json" forHTTPHeaderField:@"Content-Type"];
        [request setHTTPBody:jsonData];

        NSURLSessionDataTask *task = [[NSURLSession sharedSession]
            dataTaskWithRequest:request
            completionHandler:^(NSData *data, NSURLResponse *response, NSError *error) {
                if (error) {
                    NSLog(@"Error: %@", error);
                    return;
                }
                NSString *result = [[NSString alloc] initWithData:data
                                                        encoding:NSUTF8StringEncoding];
                NSLog(@"%@", result);
            }];

        [task resume];
        [[NSRunLoop mainRunLoop] run];
    }
    return 0;
}
(* Requires cohttp and yojson libraries *)
open Lwt
open Cohttp
open Cohttp_lwt_unix

let url = "https://anyrouter.win/v1/messages"
let api_key = Sys.getenv "API_KEY"

let payload = {|{
  "model": "claude-sonnet-4-5-20250929",
  "max_tokens": 1024,
  "messages": [
    {
      "role": "user",
      "content": "Hello, world"
    }
  ]
}|}

let () =
  let headers = Header.init ()
    |> fun h -> Header.add h "x-api-key" api_key
    |> fun h -> Header.add h "anthropic-version" "2025-10-01"
    |> fun h -> Header.add h "Content-Type" "application/json"
  in
  let body = Cohttp_lwt.Body.of_string payload in

  let response = Client.post ~headers ~body (Uri.of_string url) >>= fun (resp, body) ->
    body |> Cohttp_lwt.Body.to_string >|= fun body_str ->
    print_endline body_str
  in
  Lwt_main.run response
import 'dart:convert';
import 'dart:io';
import 'package:http/http.dart' as http;

void main() async {
  final url = Uri.parse('https://anyrouter.win/v1/messages');
  final apiKey = Platform.environment['API_KEY'];

  final payload = {
    'model': 'claude-sonnet-4-5-20250929',
    'max_tokens': 1024,
    'messages': [
      {
        'role': 'user',
        'content': 'Hello, world'
      }
    ]
  };

  final response = await http.post(
    url,
    headers: {
      'x-api-key': apiKey!,
      'anthropic-version': '2025-10-01',
      'Content-Type': 'application/json',
    },
    body: jsonEncode(payload),
  );

  print(response.body);
}
library(httr)
library(jsonlite)

url <- "https://anyrouter.win/v1/messages"
api_key <- Sys.getenv("API_KEY")

payload <- list(
  model = "claude-sonnet-4-5-20250929",
  max_tokens = 1024,
  messages = list(
    list(
      role = "user",
      content = "Hello, world"
    )
  )
)

response <- POST(
  url,
  add_headers(
    `x-api-key` = api_key,
    `anthropic-version` = "2025-10-01",
    `Content-Type` = "application/json"
  ),
  body = toJSON(payload, auto_unbox = TRUE),
  encode = "raw"
)

cat(content(response, "text"))
{
  "code": 200,
  "data": {
    "id": "msg_013Zva2CMHLNnXjNJJKqJ2EF",
    "type": "message",
    "role": "assistant",
    "content": [
      {
        "type": "text",
        "text": "Hello! I'm Claude. Nice to meet you."
      }
    ],
    "model": "claude-sonnet-4-5-20250929",
    "stop_reason": "end_turn",
    "stop_sequence": null,
    "usage": {
      "input_tokens": 12,
      "output_tokens": 18
    }
  }
}
{
  "type": "error",
  "error": {
    "type": "invalid_request_error",
    "message": "Invalid request parameters"
  }
}
{
  "type": "error",
  "error": {
    "type": "authentication_error",
    "message": "Invalid API key"
  }
}
{
  "type": "error",
  "error": {
    "type": "rate_limit_error",
    "message": "Too many requests"
  }
}
{
  "type": "error",
  "error": {
    "type": "api_error",
    "message": "Internal server error"
  }
}

Authorizations

stringrequired

API key used for authentication

Visit the API key management page to get your API key

Add it to the request headers:

x-api-key: YOUR_API_KEY
stringrequired

API version

Specifies the version of the Claude API to use

Example: 2025-10-01

Body

stringrequired

Model name

  • claude-haiku-4-5-20251001 - Claude 4.5 fast-response model
  • claude-sonnet-4-5-20250929 - Claude 4.5 balanced model
  • claude-opus-4-1-20250805 - The most powerful Claude 4.1 flagship model
  • claude-opus-4-1-20250805-thinking - Claude 4.1 Opus extended thinking edition
  • claude-sonnet-4-5-20250929-thinking - Claude 4.5 Sonnet extended thinking edition
arrayrequired

Message list

An array of messages; the model generates the next reply based on them. Each message contains a role and a content field.

Field details
stringrequired

Role type

Allowed values: user (user message), assistant (AI reply, used for multi-turn conversations and prefilling)

Note: the Claude API takes the system prompt via a separate system parameter, not inside messages

stringrequired

Message content

The text content of the message

Single user message example:

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

Multi-turn conversation example:

[
  { "role": "user", "content": "Hello" },
  { "role": "assistant", "content": "Hello! I'm Claude." },
  { "role": "user", "content": "Can you explain AI?" }
]

Prefilling the assistant reply:

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

Maximum number of tokens to generate

The maximum number of tokens to generate before stopping. The model may stop before reaching this limit.

Different models have different maximums; see the model documentation. Minimum: 1

string | array

System prompt

The system prompt sets Claude's role, personality, goals, and instructions.

String format:

{
  "system": "You are a professional Python programming tutor"
}

Structured format:

{
  "system": [
    {
      "type": "text",
      "text": "You are a professional Python programming tutor"
    }
  ]
}
number

Temperature, range 0-1

Controls output randomness:

  • Lower values (e.g. 0.2): more deterministic and conservative
  • Higher values (e.g. 0.8): more random and creative

Default: 1.0

number

Nucleus sampling parameter, range 0-1

Uses nucleus sampling. Use either temperature or top_p, not both at the same time.

Default: 1.0

integer

Top-K sampling

Samples only from the K highest-probability options, used to remove "long tail" low-probability responses.

Recommended for advanced use cases only.

boolean

Whether to enable streaming output

When set to true, the response is streamed back using server-sent events (SSE).

Default: false

array

Stop sequences

Custom text sequences that cause the model to stop generating when encountered.

Up to 4 sequences.

Example: ["\n\nHuman:", "\n\nAssistant:"]

object

Metadata

A metadata object for the request.

Contains:

  • user_id: user identifier
array

Tool definitions

A list of tools the model may call to complete tasks.

Function tool example:

{
  "tools": [
    {
      "name": "get_weather",
      "description": "Get the current weather for a given location",
      "input_schema": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "City and province, e.g. Beijing"
          },
          "unit": {
            "type": "string",
            "enum": ["celsius", "fahrenheit"],
            "description": "Temperature unit"
          }
        },
        "required": ["location"]
      }
    }
  ]
}

Supported tool types:

  • Custom function tools
  • Computer use tool (computer_20241022)
  • Text editor tool (text_editor_20241022)
  • Bash tool (bash_20241022)
object

Tool choice strategy

Controls how the model uses tools:

  • {"type": "auto"}: decide automatically (default)
  • {"type": "any"}: must use a tool
  • {"type": "tool", "name": "tool_name"}: use the specified tool

Response

idstring

Unique message identifier

Example: "msg_013Zva2CMHLNnXjNJJKqJ2EF"

typestring

Object type

Always "message"

rolestring

Role

Always "assistant"

contentarray

Array of content blocks

The content generated by the model, as an array of content blocks.

Text content:

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

Tool use:

[
  {
    "type": "tool_use",
    "id": "toolu_01A09q90qw90lq917835lq9",
    "name": "get_weather",
    "input": { "location": "Beijing", "unit": "celsius" }
  }
]

Content types:

  • text: text content
  • tool_use: tool invocation
modelstring

The model that handled the request

Example: "claude-sonnet-4-5-20250929"

stop_reasonstring

Stop reason

Possible values:

  • end_turn: natural end of turn
  • max_tokens: reached the maximum token limit
  • stop_sequence: hit a stop sequence
  • tool_use: invoked a tool
stop_sequencestring | null

The stop sequence that was triggered

If generation stopped because of a stop sequence, this contains that sequence; otherwise null

usageobject

Token usage statistics

Properties
input_tokensinteger

Number of input tokens

output_tokensinteger

Number of output tokens

Usage Examples

Basic Conversation

import anthropic

client = anthropic.Anthropic(
    api_key="YOUR_API_KEY",
    base_url="https://anyrouter.win"
)

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Explain the basic principles of quantum computing"}
    ]
)

print(message.content[0].text)

Multi-turn Conversation

messages = [
    {"role": "user", "content": "What is machine learning?"},
    {"role": "assistant", "content": "Machine learning is a branch of artificial intelligence..."},
    {"role": "user", "content": "Can you give a real-world example?"}
]

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=messages
)

Using a System Prompt

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    system="You are a senior Python development expert, skilled at code review and optimization advice.",
    messages=[
        {"role": "user", "content": "How can I optimize this code?\n\n[code]"}
    ]
)

Streaming Responses

with client.messages.stream(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Write a short essay about AI"}
    ]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Tool Use

tools = [
    {
        "name": "get_stock_price",
        "description": "Get the real-time price of a stock",
        "input_schema": {
            "type": "object",
            "properties": {
                "ticker": {
                    "type": "string",
                    "description": "Stock ticker symbol, e.g. AAPL"
                }
            },
            "required": ["ticker"]
        }
    }
]

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    tools=tools,
    messages=[
        {"role": "user", "content": "What is Tesla's stock price?"}
    ]
)

# Handle the tool call
if message.stop_reason == "tool_use":
    tool_use = next(block for block in message.content if block.type == "tool_use")
    print(f"Tool called: {tool_use.name}")
    print(f"Input: {tool_use.input}")

Vision

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "url",
                        "url": "https://example.com/image.jpg"
                    }
                },
                {
                    "type": "text",
                    "text": "Describe this image"
                }
            ]
        }
    ]
)

Base64 Images

import base64

with open("image.jpg", "rb") as image_file:
    image_data = base64.b64encode(image_file.read()).decode("utf-8")

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/jpeg",
                        "data": image_data
                    }
                },
                {
                    "type": "text",
                    "text": "Analyze this image"
                }
            ]
        }
    ]
)

Best Practices

1. Prompt Engineering

Clear role definition:

system = """You are an experienced chief data scientist. Your expertise includes:
- In-depth statistical analysis and interactive data visualization
- Full-lifecycle machine learning model development, with deep mastery of Python and R
- All output must strictly follow scientific accuracy and professional engineering standards
Please provide professional, accurate advice."""

Structured output:

message = "Return the analysis results in JSON format, including summary, key_findings, and recommendations fields."

2. Error Handling

from anthropic import APIError, RateLimitError

try:
    message = client.messages.create(
        model="claude-sonnet-4-5-20250929",
        max_tokens=1024,
        messages=[{"role": "user", "content": "Hello"}]
    )
except RateLimitError:
    print("Rate limited, please retry later")
except APIError as e:
    print(f"API error: {e}")

3. Token Optimization

# Use shorter prompts
messages = [
    {"role": "user", "content": "Summarize the key points:\n\n[long text]"}
]

# Limit output length
message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=500,  # limit the output
    messages=messages
)

4. Prefilled Responses

# Guide the model to reply in a specific format
messages = [
    {"role": "user", "content": "List 5 Python best practices"},
    {"role": "assistant", "content": "Here are 5 Python best practices:\n\n1."}
]

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=messages
)

Handling Streaming Responses

Python Streaming Example

import anthropic

client = anthropic.Anthropic(
    api_key="YOUR_API_KEY",
    base_url="https://anyrouter.win"
)

with client.messages.stream(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Write a Python decorator example"}
    ]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

JavaScript Streaming Example

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: process.env.API_KEY,
  baseURL: "https://anyrouter.win",
});

const stream = await client.messages.stream({
  model: "claude-sonnet-4-5-20250929",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Write a React component example" }],
});

for await (const chunk of stream) {
  if (
    chunk.type === "content_block_delta" &&
    chunk.delta.type === "text_delta"
  ) {
    process.stdout.write(chunk.delta.text);
  }
}

Notes

  1. API key security:

    • Store API keys in environment variables
    • Do not hardcode keys in your code
    • Rotate keys regularly
  2. Rate limits:

    • Be aware of API rate limits
    • Implement retry logic
    • Use an exponential backoff strategy
  3. Token management:

    • Monitor token usage
    • Optimize prompt length
    • Use an appropriate max_tokens value
  4. Model selection:

    • Opus: complex tasks that need deep reasoning
    • Sonnet: balanced performance and cost
    • Haiku: fast responses for simple tasks
  5. Content filtering:

    • Validate user input
    • Filter sensitive information
    • Implement content moderation

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