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Claude Fable 5 AIチャットプレイグラウンドとAPI

詳細な執筆、丁寧な要約、高品質な長文作業のためにClaude Fable 5をオンラインで試してください。APIを統合する前に、トークン料金を確認し、プロンプトをテストできます。

入力公式 $10.00 100万トークンあたりAIReiter $5.00 100万トークンあたり出力公式 $50.00 100万トークンあたりAIReiter $25.00 100万トークンあたりキャッシュ読み取り公式 $1.00 100万トークンあたりAIReiter $0.50 100万トークンあたりキャッシュ作成公式 $12.50 100万トークンあたりAIReiter $6.25 100万トークンあたり
モデルタイプ
APIで実行
PlaygroundReadmeAPI

入力

imagefile[]
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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-fable-5:

const message = await client.messages.create({
    "model": "claude-fable-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-fable-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-fable-5:

message = client.messages.create(
      model = "claude-fable-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-fable-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-fable-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-fable-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-fable-5",
  "input": {
    "model": "claude-fable-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-fable-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
500 credits / 100万トークン
出力 Token
2,500 credits / 100万トークン
キャッシュ読み取り
50 credits / 100万トークン
キャッシュ書き込み
625 credits / 100万トークン

Claude Fable 5でできること

構成、文体、統合、修正の品質が素早い初稿より重要な、ニュアンスのある長文作業にはClaude Fable 5を選びましょう。

長文作成

一貫した構成と文体で、まとまりのあるレポート、物語、ドキュメントを作成します。

ニュアンスを保ったリライト

意味を保ちながら、トーン、対象読者、長さ、編集上の重点を変えます。

ソースの統合

重要な違いを失わずに、複数の文書を読みやすいブリーフにまとめます。

編集レビュー

より優れた下書きを作成する前に、構成、明確さ、つながり、論点の質を評価します。

Claude Fable 5のユースケース

ニュアンス、一貫性、丁寧な修正が評価される、編集・リサーチ・コミュニケーションのワークフローに最適です。
01

レポートとホワイトペーパー

一貫した論旨と用語で長文ドキュメントを構築します。

02

ブランドおよび編集業務

想定読者に合わせて文体と構成を調整します。

03

リサーチブリーフ

不確実性と出典の違いを保ちながら、知見を統合します。

04

文書修正

批評、再構成、リライトを通じて下書きを改善します。

Claude Fable 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 Fable 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 Fable 5 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 Fable 5は何に最適ですか?

長文作成、ニュアンスを保ったリライト、統合、編集レビュー向けにここで位置づけられています。

/ 02

Claude Fable 5は特定の文体を保てますか?

代表的な例と明示的な文体制約を提示し、プレイグラウンドで修正版を比較してください。

/ 03

Claude Fable 5は創作向けだけですか?

いいえ。レポート、リサーチブリーフ、ドキュメント、その他の構造化された長文作業にも評価できます。

/ 04

Claude Fable 5の料金はどのように設定されていますか?

現在の入力トークン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 Fable 5をAPI経由で使用できますか?

はい。リンク先のAPIドキュメントに従い、ページのモデルIDを使用してください。

AIREITER

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