メッセージの作成（Claude）

curl --request POST \
  --url https://api.mixroute.ai/v1/messages \
  --header 'Authorization: Bearer sk-xxxxxxxxxx' \
  --header 'Content-Type: application/json' \
  --header 'anthropic-version: 2023-06-01' \
  --data '{
    "model": "claude-opus-4-8",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Briefly explain artificial intelligence"}
    ]
  }'

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.mixroute.ai"
)

message = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Briefly explain artificial intelligence"}
    ]
)
print(message.content[0].text)

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

const client = new Anthropic({
  apiKey: 'sk-xxxxxxxxxx',
  baseURL: 'https://api.mixroute.ai'
});

const message = await client.messages.create({
  model: 'claude-opus-4-8',
  max_tokens: 1024,
  messages: [
    { role: 'user', content: 'Briefly explain artificial intelligence' }
  ]
});
console.log(message.content[0].text);

<?php
$client = new GuzzleHttp\Client();
$response = $client->post('https://api.mixroute.ai/v1/messages', [
    'headers' => [
        'Authorization' => 'Bearer sk-xxxxxxxxxx',
        'Content-Type' => 'application/json',
        'anthropic-version' => '2023-06-01',
    ],
    'json' => [
        'model' => 'claude-opus-4-8',
        'max_tokens' => 1024,
        'messages' => [
            ['role' => 'user', 'content' => 'Briefly explain artificial intelligence']
        ]
    ]
]);
echo $response->getBody();

package main

import (
    "bytes"
    "encoding/json"
    "net/http"
)

func main() {
    payload := map[string]interface{}{
        "model": "claude-opus-4-8",
        "max_tokens": 1024,
        "messages": []map[string]string{
            {"role": "user", "content": "Briefly explain artificial intelligence"},
        },
    }
    body, _ := json.Marshal(payload)
    req, _ := http.NewRequest("POST", "https://api.mixroute.ai/v1/messages", bytes.NewBuffer(body))
    req.Header.Set("Authorization", "Bearer sk-xxxxxxxxxx")
    req.Header.Set("Content-Type", "application/json")
    req.Header.Set("anthropic-version", "2023-06-01")
    http.DefaultClient.Do(req)
}

import java.net.http.*;
import java.net.URI;

HttpClient client = HttpClient.newHttpClient();
String json = """
    {
        "model": "claude-opus-4-8",
        "max_tokens": 1024,
        "messages": [{"role": "user", "content": "Briefly explain artificial intelligence"}]
    }
    """;
HttpRequest request = HttpRequest.newBuilder()
    .uri(URI.create("https://api.mixroute.ai/v1/messages"))
    .header("Authorization", "Bearer sk-xxxxxxxxxx")
    .header("Content-Type", "application/json")
    .header("anthropic-version", "2023-06-01")
    .POST(HttpRequest.BodyPublishers.ofString(json))
    .build();
HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());

require 'net/http'
require 'json'

uri = URI('https://api.mixroute.ai/v1/messages')
http = Net::HTTP.new(uri.host, uri.port)
http.use_ssl = true

request = Net::HTTP::Post.new(uri)
request['Authorization'] = 'Bearer sk-xxxxxxxxxx'
request['Content-Type'] = 'application/json'
request['anthropic-version'] = '2023-06-01'
request.body = {
  model: 'claude-opus-4-8',
  max_tokens: 1024,
  messages: [{ role: 'user', content: 'Briefly explain artificial intelligence' }]
}.to_json

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

{
  "id": "msg_xxx",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Artificial intelligence is a new technical science that researches and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence..."
    }
  ],
  "model": "claude-opus-4-8",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 25,
    "output_tokens": 100
  }
}

POST

messages

curl --request POST \
  --url https://api.mixroute.ai/v1/messages \
  --header 'Authorization: Bearer sk-xxxxxxxxxx' \
  --header 'Content-Type: application/json' \
  --header 'anthropic-version: 2023-06-01' \
  --data '{
    "model": "claude-opus-4-8",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Briefly explain artificial intelligence"}
    ]
  }'

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.mixroute.ai"
)

message = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Briefly explain artificial intelligence"}
    ]
)
print(message.content[0].text)

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

const client = new Anthropic({
  apiKey: 'sk-xxxxxxxxxx',
  baseURL: 'https://api.mixroute.ai'
});

const message = await client.messages.create({
  model: 'claude-opus-4-8',
  max_tokens: 1024,
  messages: [
    { role: 'user', content: 'Briefly explain artificial intelligence' }
  ]
});
console.log(message.content[0].text);

<?php
$client = new GuzzleHttp\Client();
$response = $client->post('https://api.mixroute.ai/v1/messages', [
    'headers' => [
        'Authorization' => 'Bearer sk-xxxxxxxxxx',
        'Content-Type' => 'application/json',
        'anthropic-version' => '2023-06-01',
    ],
    'json' => [
        'model' => 'claude-opus-4-8',
        'max_tokens' => 1024,
        'messages' => [
            ['role' => 'user', 'content' => 'Briefly explain artificial intelligence']
        ]
    ]
]);
echo $response->getBody();

package main

import (
    "bytes"
    "encoding/json"
    "net/http"
)

func main() {
    payload := map[string]interface{}{
        "model": "claude-opus-4-8",
        "max_tokens": 1024,
        "messages": []map[string]string{
            {"role": "user", "content": "Briefly explain artificial intelligence"},
        },
    }
    body, _ := json.Marshal(payload)
    req, _ := http.NewRequest("POST", "https://api.mixroute.ai/v1/messages", bytes.NewBuffer(body))
    req.Header.Set("Authorization", "Bearer sk-xxxxxxxxxx")
    req.Header.Set("Content-Type", "application/json")
    req.Header.Set("anthropic-version", "2023-06-01")
    http.DefaultClient.Do(req)
}

import java.net.http.*;
import java.net.URI;

HttpClient client = HttpClient.newHttpClient();
String json = """
    {
        "model": "claude-opus-4-8",
        "max_tokens": 1024,
        "messages": [{"role": "user", "content": "Briefly explain artificial intelligence"}]
    }
    """;
HttpRequest request = HttpRequest.newBuilder()
    .uri(URI.create("https://api.mixroute.ai/v1/messages"))
    .header("Authorization", "Bearer sk-xxxxxxxxxx")
    .header("Content-Type", "application/json")
    .header("anthropic-version", "2023-06-01")
    .POST(HttpRequest.BodyPublishers.ofString(json))
    .build();
HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());

require 'net/http'
require 'json'

uri = URI('https://api.mixroute.ai/v1/messages')
http = Net::HTTP.new(uri.host, uri.port)
http.use_ssl = true

request = Net::HTTP::Post.new(uri)
request['Authorization'] = 'Bearer sk-xxxxxxxxxx'
request['Content-Type'] = 'application/json'
request['anthropic-version'] = '2023-06-01'
request.body = {
  model: 'claude-opus-4-8',
  max_tokens: 1024,
  messages: [{ role: 'user', content: 'Briefly explain artificial intelligence' }]
}.to_json

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

{
  "id": "msg_xxx",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Artificial intelligence is a new technical science that researches and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence..."
    }
  ],
  "model": "claude-opus-4-8",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 25,
    "output_tokens": 100
  }
}

はじめに

Messages APIは、MixRouteを通じてAnthropic Claudeのネイティブインターフェースとの互換性を提供し、AnthropicのSDKやClaude Codeなどのツールを直接利用できるようにします。このインターフェースはAnthropicのAPI仕様に準拠し、拡張思考、ツール呼び出し、その他の高度な機能を含むClaudeモデルの全機能を提供します。 OpenAI互換クライアント（OpenAI SDKなど）を使用している場合は、代わりに/v1/chat/completionsエンドポイントを使用することをおすすめします。

Claude Sonnet 5.5にはモデル固有の要件があります。適応型思考はデフォルトで有効です。最初の思考を無効にするには、effortをhigh以下にしてthinking.type=between_toolsを使用してください。tool_choice.typeにはany/toolではなくautoを使用し、ツールループでは思考ブロックと署名を完全な状態で保持してください。以前の例を再利用する前に、Sonnet 5.5を確認してください。

認証

Bearerトークン。例: Bearer sk-xxxxxxxxxx

リクエストパラメーター

string

必須

Claudeのモデル識別子。例: claude-opus-4-8

array

必須

会話メッセージのリスト。各メッセージにはrole（user/assistant）とcontentが含まれます

integer

必須

生成するトークンの最大数。0より大きい必要があります

string | array

システムプロンプト。文字列形式または配列形式（プロンプトキャッシュ用）をサポートします。

boolean

ストリーミング出力を有効にする

number

サンプリング温度。範囲は0～1

number

Nucleusサンプリングのパラメーター。範囲は0～1

integer

Top-kサンプリングパラメータ

array

カスタム停止シーケンス

object

拡張思考の設定。typeとbudget_tokensを含みます

array

ツール定義の一覧

基本的な例

非ストリーミングリクエスト
ストリーミングリクエスト（SSE）
Pythonの例

curl -X POST "https://api.mixroute.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-4-8",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Briefly explain artificial intelligence"}
    ]
  }'

curl -X POST "https://api.mixroute.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-4-8",
    "max_tokens": 1024,
    "stream": true,
    "messages": [
      {"role": "user", "content": "Briefly explain artificial intelligence"}
    ]
  }'

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.mixroute.ai"
)

# Non-streaming
message = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Briefly explain artificial intelligence"}
    ]
)
print(message.content[0].text)

# Streaming
with client.messages.stream(
    model="claude-opus-4-8",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Briefly explain artificial intelligence"}
    ]
) as stream:
    for text_block in stream.text_stream:
        print(text_block, end="")

高度な機能

システムプロンプト

文字列形式
配列形式

curl -X POST "https://api.mixroute.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-4-8",
    "max_tokens": 1024,
    "system": "You are a professional programming assistant skilled at explaining complex technical concepts.",
    "messages": [
      {"role": "user", "content": "What is recursion?"}
    ]
  }'

curl -X POST "https://api.mixroute.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-4-8",
    "max_tokens": 1024,
    "system": [
      {
        "type": "text",
        "text": "You are a professional programming assistant skilled at explaining complex technical concepts."
      }
    ],
    "messages": [
      {"role": "user", "content": "What is recursion?"}
    ]
  }'

拡張思考

基本的な使い方
Pythonの例

curl -X POST "https://api.mixroute.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-4-8",
    "max_tokens": 16000,
    "thinking": {
      "type": "enabled",
      "budget_tokens": 10000
    },
    "messages": [
      {"role": "user", "content": "Provide a medium difficulty geometry problem and solve it step by step"}
    ]
  }'

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.mixroute.ai"
)

with client.messages.stream(
    model="claude-opus-4-8",
    max_tokens=16000,
    thinking={
        "type": "enabled",
        "budget_tokens": 10000
    },
    messages=[
        {"role": "user", "content": "Provide a medium difficulty geometry problem and solve it step by step"}
    ]
) as stream:
    for event in stream:
        if event.type == "content_block_delta":
            if hasattr(event.delta, "thinking"):
                print(f"[Thinking] {event.delta.thinking}", end="")
            elif hasattr(event.delta, "text"):
                print(event.delta.text, end="")

ツール

関数ツール
Claude公式ウェブ検索ツール

curl -X POST "https://api.mixroute.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-4-8",
    "max_tokens": 1024,
    "tools": [
      {
        "name": "get_weather",
        "description": "Get weather information for a city",
        "input_schema": {
          "type": "object",
          "properties": {
            "city": {
              "type": "string",
              "description": "City name"
            }
          },
          "required": ["city"]
        }
      }
    ],
    "messages": [
      {"role": "user", "content": "What is the weather like in Tokyo?"}
    ]
  }'

curl -X POST "https://api.mixroute.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-4-8",
    "max_tokens": 4096,
    "tools": [
      {
        "type": "web_search_20250305",
        "name": "web_search",
        "max_uses": 5
      }
    ],
    "messages": [
      {"role": "user", "content": "What are the latest news about artificial intelligence?"}
    ]
  }'

マルチモーダル入力（画像）

curl -X POST "https://api.mixroute.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-4-8",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "image",
            "source": {
              "type": "base64",
              "media_type": "image/jpeg",
              "data": "base64_encoded_image_data"
            }
          },
          {
            "type": "text",
            "text": "Please describe this image"
          }
        ]
      }
    ]
  }'

プロンプトキャッシュ

よく使用するコンテキストの内容をキャッシュすると、コストを大幅に削減し、応答速度を向上できます。キャッシュする内容には、最低1024トークンが必要です。

システムキャッシュ
メッセージのキャッシュ
Python SDKの例

curl -X POST "https://api.mixroute.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-4-8",
    "max_tokens": 1024,
    "system": [
      {
        "type": "text",
        "text": "You are a professional document analysis assistant. Here is the document content to analyze: [long text content, at least 1024 tokens]",
        "cache_control": {"type": "ephemeral"}
      }
    ],
    "messages": [
      {"role": "user", "content": "Please summarize the main points of the document"}
    ]
  }'

curl -X POST "https://api.mixroute.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-4-8",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Here is the codebase to analyze: [large code content, at least 1024 tokens]",
            "cache_control": {"type": "ephemeral"}
          },
          {
            "type": "text",
            "text": "Please identify potential issues in the code"
          }
        ]
      }
    ]
  }'

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.mixroute.ai"
)

# System cache
message = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are a professional document analysis assistant. Here is the document content to analyze: [long text content]",
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[
        {"role": "user", "content": "Please summarize the main points of the document"}
    ]
)

# Check cache usage
print(f"Cache creation tokens: {message.usage.cache_creation_input_tokens}")
print(f"Cache read tokens: {message.usage.cache_read_input_tokens}")

レスポンス形式

非ストリーミングレスポンス
ストリーミングレスポンス

{
  "id": "msg_xxx",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Response content..."
    }
  ],
  "model": "claude-opus-4-8",
  "stop_reason": "end_turn",
  "usage": {
    "input_tokens": 25,
    "output_tokens": 100,
    "cache_creation_input_tokens": 0,
    "cache_read_input_tokens": 0
  }
}

event: message_start
data: {"type":"message_start","message":{"id":"msg_xxx","type":"message","role":"assistant","content":[],"model":"claude-opus-4-8"}}

event: content_block_start
data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Response"}}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" content"}}

event: content_block_stop
data: {"type":"content_block_stop","index":0}

event: message_delta
data: {"type":"message_delta","delta":{"stop_reason":"end_turn"},"usage":{"output_tokens":100}}

event: message_stop
data: {"type":"message_stop"}

curl --request POST \
  --url https://api.mixroute.ai/v1/messages \
  --header 'Authorization: Bearer sk-xxxxxxxxxx' \
  --header 'Content-Type: application/json' \
  --header 'anthropic-version: 2023-06-01' \
  --data '{
    "model": "claude-opus-4-8",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Briefly explain artificial intelligence"}
    ]
  }'

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.mixroute.ai"
)

message = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Briefly explain artificial intelligence"}
    ]
)
print(message.content[0].text)

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

const client = new Anthropic({
  apiKey: 'sk-xxxxxxxxxx',
  baseURL: 'https://api.mixroute.ai'
});

const message = await client.messages.create({
  model: 'claude-opus-4-8',
  max_tokens: 1024,
  messages: [
    { role: 'user', content: 'Briefly explain artificial intelligence' }
  ]
});
console.log(message.content[0].text);

<?php
$client = new GuzzleHttp\Client();
$response = $client->post('https://api.mixroute.ai/v1/messages', [
    'headers' => [
        'Authorization' => 'Bearer sk-xxxxxxxxxx',
        'Content-Type' => 'application/json',
        'anthropic-version' => '2023-06-01',
    ],
    'json' => [
        'model' => 'claude-opus-4-8',
        'max_tokens' => 1024,
        'messages' => [
            ['role' => 'user', 'content' => 'Briefly explain artificial intelligence']
        ]
    ]
]);
echo $response->getBody();

package main

import (
    "bytes"
    "encoding/json"
    "net/http"
)

func main() {
    payload := map[string]interface{}{
        "model": "claude-opus-4-8",
        "max_tokens": 1024,
        "messages": []map[string]string{
            {"role": "user", "content": "Briefly explain artificial intelligence"},
        },
    }
    body, _ := json.Marshal(payload)
    req, _ := http.NewRequest("POST", "https://api.mixroute.ai/v1/messages", bytes.NewBuffer(body))
    req.Header.Set("Authorization", "Bearer sk-xxxxxxxxxx")
    req.Header.Set("Content-Type", "application/json")
    req.Header.Set("anthropic-version", "2023-06-01")
    http.DefaultClient.Do(req)
}

import java.net.http.*;
import java.net.URI;

HttpClient client = HttpClient.newHttpClient();
String json = """
    {
        "model": "claude-opus-4-8",
        "max_tokens": 1024,
        "messages": [{"role": "user", "content": "Briefly explain artificial intelligence"}]
    }
    """;
HttpRequest request = HttpRequest.newBuilder()
    .uri(URI.create("https://api.mixroute.ai/v1/messages"))
    .header("Authorization", "Bearer sk-xxxxxxxxxx")
    .header("Content-Type", "application/json")
    .header("anthropic-version", "2023-06-01")
    .POST(HttpRequest.BodyPublishers.ofString(json))
    .build();
HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());

require 'net/http'
require 'json'

uri = URI('https://api.mixroute.ai/v1/messages')
http = Net::HTTP.new(uri.host, uri.port)
http.use_ssl = true

request = Net::HTTP::Post.new(uri)
request['Authorization'] = 'Bearer sk-xxxxxxxxxx'
request['Content-Type'] = 'application/json'
request['anthropic-version'] = '2023-06-01'
request.body = {
  model: 'claude-opus-4-8',
  max_tokens: 1024,
  messages: [{ role: 'user', content: 'Briefly explain artificial intelligence' }]
}.to_json

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

{
  "id": "msg_xxx",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Artificial intelligence is a new technical science that researches and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence..."
    }
  ],
  "model": "claude-opus-4-8",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 25,
    "output_tokens": 100
  }
}

人気モデル

OpenAI SDKの使い方

システムAPI

テキストシリーズ

意思決定モデル

埋め込みシリーズ

画像シリーズ

動画シリーズ

その他

メッセージの作成（Claude）

はじめに

認証

リクエストパラメーター

基本的な例

高度な機能

システムプロンプト

拡張思考

ツール

マルチモーダル入力（画像）

プロンプトキャッシュ

レスポンス形式

​はじめに

​認証

​リクエストパラメーター

​基本的な例

​高度な機能

​システムプロンプト

​拡張思考

​ツール

​マルチモーダル入力（画像）

​プロンプトキャッシュ

​レスポンス形式

はじめに

認証

リクエストパラメーター

基本的な例

高度な機能

システムプロンプト

拡張思考

ツール

マルチモーダル入力（画像）

プロンプトキャッシュ

レスポンス形式