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Claude Opus 4.8 AI チャットプレイグラウンドと API

透明なトークン課金で、エージェント的コーディング、難解なデバッグ、複雑な推論、専門的な知識業務に Claude Opus 4.8 をオンラインで試してみましょう。

入力公式 $5.00 100万トークンあたりAIReiter $3.50 100万トークンあたり出力公式 $25.00 100万トークンあたりAIReiter $17.50 100万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 $0.35 100万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 $4.38 100万トークンあたり
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-4-8:

const message = await client.messages.create({
    "model": "claude-opus-4-8",
    "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-4-8",
    "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-4-8:

message = client.messages.create(
      model = "claude-opus-4-8",
      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-4-8",
      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-4-8 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-4-8",
  "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-4-8",
  "input": {
    "model": "claude-opus-4-8",
    "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-4-8
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
350 credits / 100万トークン
出力 Token
1,750 credits / 100万トークン
キャッシュ読み取り
35 credits / 100万トークン
キャッシュ書き込み
437.5 credits / 100万トークン

Claude Opus 4.8 でできること

慎重な段階的作業が効果を発揮する、難しいコーディング、デバッグ、エージェント計画、専門的分析には Claude Opus 4.8 が最適です。

エージェント的コーディング

制約条件と過去の結果を踏まえながら、複数ファイルにまたがるコーディング作業を計画し、実行します。

難解なデバッグ

コンポーネントをまたいで障害を追跡し、仮説を検証し、最も可能性の高い根本原因を説明します。

アーキテクチャ推論

実装前に、システム境界、移行計画、技術的トレードオフを評価します。

専門的分析

詳細な技術的・運用的・知識集約型の課題について、明示的な推論を伴って検討します。

Claude Opus 4.8 のユースケース

迅速な一般回答よりも、慎重な分析が必要なシニアエンジニアリングや知識ワークフローに最適です。
01

大規模リファクタリング

個々のファイルを編集する前に、システム全体に及ぶ変更を検討します。

02

根本原因分析

難解な不具合について、競合する説明を検証します。

03

技術設計レビュー

前提を問い直し、アーキテクチャの選択肢を比較します。

04

知識集約型業務

詳細なソース資料を分析し、専門的な応答を作成します。

Claude Opus 4.8 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 4.8 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 4.8 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 4.8 は何に最適ですか?

エージェント的コーディング、難解なデバッグ、アーキテクチャ推論、専門的な知識業務に適しています。

/ 02

Claude Opus 4.8 は大規模リファクタリングに役立ちますか?

システム全体の文脈、依存関係の分析、明確な移行計画が必要なリファクタリングでは、有力な選択肢です。

/ 03

簡単なチャットに Opus 4.8 を使うべきですか?

通常はおすすめしません。短時間で済む定型的なリクエストや、低遅延が重要なリクエストには、より軽量なプランのほうが経済的です。

/ 04

Claude Opus 4.8 の料金はどのように設定されていますか?

入力および出力トークンの料金は、プレイグラウンドの上に表示されています。

/ 05

Claude Opus 4.8 を API 経由で呼び出せますか?

はい。リンク先の API ドキュメントと、このページに表示されているモデル ID を使用してください。

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