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Claude Opus 5 AIチャットプレイグラウンドとAPI

Claude Opus 5をオンラインで試して、複雑な推論、要求の高いコーディング作業、マルチステップの分析に活用しましょう。ライブのトークン価格を比較し、同じモデルをAPIワークフローに組み込みましょう。

入力公式 $5.00 100万トークンあたりAIReiter $2.50 100万トークンあたり出力公式 $25.00 100万トークンあたりAIReiter $12.50 100万トークンあたりキャッシュ読み取り公式 $0.50 100万トークンあたりAIReiter $0.25 100万トークンあたりキャッシュ作成公式 $6.25 100万トークンあたりAIReiter $3.13 100万トークンあたり
モデルタイプ
APIで実行
PlaygroundReadmeAPI

入力

imagefile[]
Optional input images sent alongside the prompt. Up to 5 files. Images are billed as input tokens.
Let the model reason before answering. The model decides how much thinking each request needs.Default: false
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Install the official Anthropic client — AIReiter speaks the same protocol, so only the base URL changes:

npm install @anthropic-ai/sdk

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

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

const client = new Anthropic({
  apiKey: process.env.AIREITER_API_KEY,
  baseURL: "https://aireiter.com/api",
});

Run claude-opus-5:

const message = await client.messages.create({
    "model": "claude-opus-5",
    "max_tokens": 4096,
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "output_config": {
      "effort": "medium"
    }
  });

console.log(message.content);

Stream the response instead:

const stream = client.messages.stream({
    "model": "claude-opus-5",
    "max_tokens": 4096,
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "output_config": {
      "effort": "medium"
    }
  });

stream.on("text", (text) => process.stdout.write(text));
const message = await stream.finalMessage();

Install the official Anthropic client — AIReiter speaks the same protocol, so only the base URL changes:

pip install anthropic

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ["AIREITER_API_KEY"],
    base_url="https://aireiter.com/api",
)

Run claude-opus-5:

message = client.messages.create(
      model = "claude-opus-5",
      max_tokens = 4096,
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      output_config = {
        effort = "medium"
      }
)

print(message.content)

Stream the response instead:

with client.messages.stream(
      model = "claude-opus-5",
      max_tokens = 4096,
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      output_config = {
        effort = "medium"
      }
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Run claude-opus-5 against AIReiter's API:

curl -s -X POST \
  -H "x-api-key: $AIREITER_API_KEY" \
  -H "Content-Type: application/json" \
  "https://aireiter.com/api/v1/messages" \
  -d '{
  "model": "claude-opus-5",
  "max_tokens": 4096,
  "messages": [
    {
      "role": "user",
      "content": "Explain what an API rate limit is and how to handle a 429 response in code."
    }
  ],
  "output_config": {
    "effort": "medium"
  }
}'

Add "stream": true to the body to receive the response as server-sent events.

出力

Example

A codebase you don't know is a risk, not a hurdle. The plan should burn down that risk in order: understand, run, trace, spike, then make the change small enough that "production-ready" is a property you can demonstrate, not a hope.

Here are the first five steps, with the evidence you collect at each.

Step 1 — Get it building and the baseline green (half day) Read the README, the manifest(s), the CI config, and any architecture docs. Boot the app locally, run the full test suite, run the linter/typechecker. Evidence: The exact commands that reproduce a clean build and a passing test suite from a fresh checkout (recorded output, not memory of it). A one-page map you wrote yourself: where the entry points are, the layers, the build/test toolchain, and the main risks to your feature (e.g. "this touches cron jobs and payments").

Step 2 — Trace the smallest end-to-end slice that resembles your feature (half day) Don't start with your feature. Pick a neighboring one that already works and follow it from entry to persistence. Note the conventions along the way: how errors surface, how config is injected, how logging is done, how tests are written. Evidence: An annotated trace — file → function → what it does — for that slice. A written list of "conventions I must follow" (not intuitions; things you observed in real code). A named shortlist of the 3–5 files you will actually touch. If you can't produce this trace, you're not ready to write code.

Step 3 — Spike the core path (day 2) Build the ugly version: hardcoded values, no error handling, no tests. The purpose is to confirm the path you traced in step 2 is real and to surface what you didn't know you didn't know. Evidence: A working spike demonstrating the feature's central data path, alongside a list of every assumption the spike broke and what you corrected. That correction list is the most valuable document in this whole plan.

Step 4 — Write the contract before the code (half of day 2 / day 3) Once the spike proves the path, pin down what production needs: the inputs/outputs, the error cases, where it sits in the conventions from step 2. Then write the tests — they'll be red, but they're the specification. Evidence: A one-to-two-page design doc, an agreed interface/API shape (with the team if there is one), and a red test suite that encodes intended behavior. If you can't write the contract without consulting the code, you haven't finished step 2.

Step 5 — Implement in small, verified increments (days 3–4) Replace the spike with the real thing in small commits, each one keeping the suite green, leaning on the existing patterns. Run lint/tests/typecheck per commit — CI, not just locally — and exercise the actual path against a real instance (staging or a local environment that isn't stubbed). Evidence: A branch with progressive commits, each green in CI; coverage on the new code; something that proves it works against reality (a test result, a log trace, a screenshot); and a review by at least one person who knows the codebase. The review counts as evidence — an unfamiliar codebase has tribal knowledge you cannot extract from the files alone.

Steps 6+ would be the things that actually make it "shipped": a migration plan and its rollback, feature flagging, observability, the release and post-release verification. But the first five get you to a reviewed, green, working slice in staging — which is the point at which you can say "this will work in production" with evidence behind it, instead of a guess.

{
  "model": "claude-opus-5",
  "input": {
    "model": "claude-opus-5",
    "max_tokens": 4096,
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "output_config": {
      "effort": "medium"
    }
  },
  "output": "A codebase you don't know is a risk, not a hurdle. The plan should burn down that risk in order: understand, run, trace, spike, then make the change small enough that \"production-ready\" is a property you can demonstrate, not a hope.\n\nHere are the first five steps, with the evidence you collect at each.\n\n**Step 1 — Get it building and the baseline green (half day)**\nRead the README, the manifest(s), the CI config, and any architecture docs. Boot the app locally, run the full test suite, run the linter/typechecker.\n*Evidence:* The exact commands that reproduce a clean build and a passing test suite from a fresh checkout (recorded output, not memory of it). A one-page map you wrote yourself: where the entry points are, the layers, the build/test toolchain, and the main risks to your feature (e.g. \"this touches cron jobs and payments\").\n\n**Step 2 — Trace the smallest end-to-end slice that resembles your feature (half day)**\nDon't start with your feature. Pick a neighboring one that already works and follow it from entry to persistence. Note the conventions along the way: how errors surface, how config is injected, how logging is done, how tests are written.\n*Evidence:* An annotated trace — file → function → what it does — for that slice. A written list of \"conventions I must follow\" (not intuitions; things you observed in real code). A named shortlist of the 3–5 files you will actually touch. If you can't produce this trace, you're not ready to write code.\n\n**Step 3 — Spike the core path (day 2)**\nBuild the ugly version: hardcoded values, no error handling, no tests. The purpose is to confirm the path you traced in step 2 is real and to surface what you didn't know you didn't know.\n*Evidence:* A working spike demonstrating the feature's central data path, alongside a list of every assumption the spike broke and what you corrected. That correction list is the most valuable document in this whole plan.\n\n**Step 4 — Write the contract before the code (half of day 2 / day 3)**\nOnce the spike proves the path, pin down what production needs: the inputs/outputs, the error cases, where it sits in the conventions from step 2. Then write the tests — they'll be red, but they're the specification.\n*Evidence:* A one-to-two-page design doc, an agreed interface/API shape (with the team if there is one), and a red test suite that encodes intended behavior. If you can't write the contract without consulting the code, you haven't finished step 2.\n\n**Step 5 — Implement in small, verified increments (days 3–4)**\nReplace the spike with the real thing in small commits, each one keeping the suite green, leaning on the existing patterns. Run lint/tests/typecheck per commit — CI, not just locally — and exercise the actual path against a real instance (staging or a local environment that isn't stubbed).\n*Evidence:* A branch with progressive commits, each green in CI; coverage on the new code; something that proves it works against reality (a test result, a log trace, a screenshot); and a review by at least one person who knows the codebase. The review counts as evidence — an unfamiliar codebase has tribal knowledge you cannot extract from the files alone.\n\nSteps 6+ would be the things that actually make it \"shipped\": a migration plan and its rollback, feature flagging, observability, the release and post-release verification. But the first five get you to a reviewed, green, working slice in staging — which is the point at which you can say \"this will work in production\" with evidence behind it, instead of a guess.",
  "metrics": {
    "input_tokens": 134,
    "output_tokens": 2354,
    "generated_in_seconds": 42.7
  },
  "example": true
}
Generated in
42.7 seconds
入力 Token
134
出力 Token
2354
Tokens per second
55.13 tokens / second
Time to first token
-

モデル詳細

Playground、APIリクエスト、社内ワークフローで同じモデルキーを使用してください。

モデル ID
claude-opus-5
プロバイダー
Anthropic
プロトコル
Anthropic Messages
コンテキストウィンドウ
1,000,000 トークン
最大出力
128,000 トークン
入力 Token
250 credits / 100万トークン
出力 Token
1,250 credits / 100万トークン
キャッシュ読み取り
25 credits / 100万トークン
キャッシュ書き込み
312.5 credits / 100万トークン

Claude Opus 5でできること

持続的な推論、慎重な判断、そして複数の依存ステップにわたる高品質な出力が必要なタスクには、Claude Opus 5を選びましょう。

複雑な推論

複数の制約、相反する証拠、依存する手順を伴う判断を進めます。

エージェント計画

計画を立て、ツールの結果を評価し、長いワークフロー全体で明確な目的を維持します。

高リスクのコードレビュー

アーキテクチャを精査し、微妙な失敗を追跡し、明確なトレードオフを伴う変更を提案します。

経営層向け要約

密度の高い技術資料やビジネス資料を、妥当性のある提言とアクションプランに変換します。

Claude Opus 5のユースケース

高コストなミス、複雑な依存関係、そして最短の応答時間よりも最終的な判断の質が重要な作業に最適です。
01

アーキテクチャの意思決定

移行パスを比較し、エンジニアリングの時間を投じる前にリスクを洗い出します。

02

複雑なインシデント分析

ログ、コード、運用コンテキストをまたいで証拠を結び付けます。

03

エージェントのオーケストレーション

複数のツールや依存する段階を含むワークフローを計画し、レビューします。

04

戦略的リサーチ

密度の高い資料を、反論可能な意思決定へと要約します。

Claude Opus 5の使い方

3 つの簡単なステップでモデルを試せます。

01

設定を選ぶ

モデルがサポートする応答コントロールとアップロードオプションを設定します。

02

プロンプトを送信

タスクを説明し、関連するコンテキストを追加して、ストリーミング応答とトークン使用量を確認します。

03

API を接続

ドキュメント化されたエンドポイントと API key を使って、同じモデルをあなたの製品に組み込みます。

Claude Opus 5 APIで構築する

予測可能な制御と使用状況レポートを備えたインタラクティブなテストから、本番統合へ進めます。

馴染みのあるプロトコル

このモデル用に設定されたAPIプロトコルを使用します。利用可能な場合はストリーミングも含まれます。

使用状況の可視化

各応答後に、入力トークン、出力トークン、および消費クレジットを追跡できます。

モデル固有のコントロール

汎用のデフォルトに頼らず、対応している生成パラメータを渡してください。

1つのアカウントと残高

同じAIReiterアカウントと請求システムで、対応しているテキストモデルをテストし、運用できます。

Claude Opus 5 に関する FAQ

オンラインplayground、料金、APIアクセスに関するよくある質問。

/ 01

Claude Opus 5はいつ選ぶべきですか?

推論の質と慎重な判断がフラッグシップモデルに見合う、複雑でマルチステップな作業に選びましょう。

/ 02

Claude Opus 5はコーディングエージェントに適していますか?

プレイグラウンドを使って、計画立案、コードレビュー、ツール駆動のワークフローを自分のリポジトリのタスクでテストできます。

/ 03

Claude Opus 5の料金はどのようになっていますか?

入力・出力トークンの料金はプレイグラウンド上部に表示されており、本番ルーティングの前に確認してください。

/ 04

すべてのリクエストで Claude Opus 5 を使うべきですか?

いいえ。定型的な処理や低遅延が求められる処理は軽量なモデルに振り分け、Opus 5 は難しいリクエストのために取っておいてください。

/ 05

Claude Opus 5 に API 経由でアクセスできますか?

はい。このページにリンクされている API ドキュメントを使用し、表示されているモデル ID を送信してください。

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