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Claude Fable 5 AI 聊天遊樂場與 API

在線試用 Claude Fable 5,體驗細緻寫作、謹慎整合與高品質長篇內容創作。在整合 API 前,先查看 token 定價並測試提示詞。

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模型類型
使用 API 執行
PlaygroundREADMEAPI

輸入

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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.

輸出

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
輸入 Token
134
輸出 Token
2354
Tokens per second
55.13 tokens / second
Time to first token
-

模型詳情

在 Playground、API 請求與內部工作流程中使用相同的模型鍵。

模型 ID
claude-fable-5
供應商
Anthropic
協定
Anthropic Messages
上下文視窗
1,000,000 Token
最大輸出
128,000 Token
輸入 Token
500 credits / 1M Token
輸出 Token
2,500 credits / 1M Token
快取讀取
50 credits / 1M Token
快取寫入
625 credits / 1M Token

Claude Fable 5 能做什麼

當您重視結構、語氣、整合與修訂品質,而非快速初稿時,請選擇 Claude Fable 5 進行細膩的長篇工作。

長篇撰寫

以一致的結構與語氣,撰寫連貫的報告、敘事與文件。

細膩改寫

在改變語氣、受眾、長度或編輯重點的同時,保留原意。

資料整合

將多份文件整合成易讀的簡報,同時不遺漏重要差異。

編輯修訂

在產出更強的草稿前,先檢視結構、清晰度、銜接與論證品質。

Claude Fable 5 使用情境

最適合重視細膩度、連貫性與審慎修訂的編輯、研究與溝通工作流程。
01

報告與白皮書

建立具備連貫論點與一致術語的長篇文件。

02

品牌與編輯工作

為特定受眾調整語氣與結構。

03

研究摘要

整合研究發現,同時保留不確定性與來源差異。

04

文件修訂

透過審閱、重組與改寫來改善草稿。

如何使用 Claude Fable 5

透過三個簡單步驟測試這個模型。

01

選擇設定

設定模型支援的回應控制與上傳選項。

02

送出提示

描述任務、加入相關脈絡,並查看串流回應與 token 使用量。

03

連接 API

使用文件中的端點與你的 API key,將同一模型整合到你的產品中。

使用 Claude Fable 5 API 開發

從互動式測試一路進階到正式整合,並享有可預測的控制與用量報告。

熟悉的協議

使用此模型設定的 API 協議,包括可用時的串流。

用量可視化

在每次回應後追蹤輸入 token、輸出 token 和已消耗的點數。

模型專屬控制

傳入支援的生成參數,而不是依賴通用預設值。

一個帳戶與餘額

透過相同的 AIReiter 帳戶與計費系統測試並操作支援的文字模型。

Claude Fable 5 常見問題

關於線上 playground、價格與 API 存取的常見問題。

/ 01

Claude Fable 5 最適合做什麼?

此處將其定位為長篇撰寫、細膩改寫、整合與編輯修訂的工具。

/ 02

Claude Fable 5 能保留特定的寫作風格嗎?

提供具代表性的範例與明確的語氣限制,然後在遊樂場中比較修訂結果。

/ 03

Claude Fable 5 只適合創意寫作嗎?

不是。它也可用於報告、研究摘要、文件,以及其他結構化的長篇工作。

/ 04

Claude Fable 5 的定價方式是什麼?

目前的輸入與輸出 token 費率顯示在 playground 上方。

/ 05

我可以透過 API 使用 Claude Fable 5 嗎?

可以。請參閱連結的 API 文件,並使用頁面上的 model ID。

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