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Claude Fable 5 AI Chat Playground 및 API

상세한 글쓰기, 신중한 종합, 고품질의 장문 작업을 위해 Claude Fable 5를 온라인에서 사용해 보세요. API를 통합하기 전에 토큰 가격을 확인하고 프롬프트를 테스트할 수 있습니다.

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모델 유형
API로 실행
플레이그라운드READMEAPI

입력

imagefile[]
Optional input images sent alongside the prompt. Up to 5 files. Images are billed as input tokens.
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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.

출력

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
입력 Token
134
출력 Token
2354
Tokens per second
55.13 tokens / second
Time to first token
-

모델 세부정보

플레이그라운드, API 요청, 내부 워크플로에서 동일한 모델 키를 사용하세요.

모델 ID
claude-fable-5
공급자
Anthropic
프로토콜
Anthropic Messages
컨텍스트 창
1,000,000 토큰
최대 출력
128,000 토큰
입력 Token
500 credits / 100만 토큰
출력 Token
2,500 credits / 100만 토큰
캐시 읽기
50 credits / 100만 토큰
캐시 쓰기
625 credits / 100만 토큰

Claude Fable 5로 할 수 있는 일

구조, 문체, 종합, 수정의 품질이 빠른 초안보다 더 중요한 미묘한 장문 작업에는 Claude Fable 5를 선택하세요.

장문 작성

일관된 구조와 문체로 일관성 있는 보고서, 서사, 문서를 작성하세요.

미묘한 재작성

의미는 유지하면서 어조, 대상 독자, 길이, 편집상의 강조점을 바꾸세요.

자료 종합

중요한 차이를 잃지 않으면서 여러 문서를 읽기 쉬운 브리프로 통합하세요.

편집 수정

더 강력한 초안을 만들기 전에 구성, 명확성, 전환, 논증의 질을 비판적으로 검토하세요.

Claude Fable 5 활용 사례

미묘함, 일관성, 신중한 수정을 중시하는 편집, 리서치, 커뮤니케이션 워크플로에 가장 적합합니다.
01

보고서 및 백서

일관된 논지와 용어로 긴 문서를 작성하세요.

02

브랜드 및 편집 작업

정해진 대상에 맞게 문체와 구조를 조정하세요.

03

리서치 브리프

불확실성과 출처의 차이를 유지하면서 결과를 통합하세요.

04

문서 수정

비평, 재구성, 재작성을 통해 초안을 개선하세요.

Claude Fable 5 사용 방법

세 가지 간단한 단계로 모델을 테스트해 보세요.

01

설정 선택

모델이 지원하는 응답 제어 및 업로드 옵션을 설정하세요.

02

프롬프트 보내기

작업을 설명하고, 관련 맥락을 추가한 뒤, 스트리밍 응답과 토큰 사용량을 검토하세요.

03

API 연결

문서화된 엔드포인트와 API 키를 사용해 동일한 모델을 제품에 가져오세요.

Claude Fable 5 API로 구축하기

예측 가능한 제어와 사용량 보고를 통해 인터랙티브 테스트에서 프로덕션 통합까지 진행하세요.

익숙한 프로토콜

사용 가능한 경우 스트리밍을 포함하여 이 모델에 구성된 API 프로토콜을 사용하세요.

사용량 가시성

각 응답 후 입력 토큰, 출력 토큰, 소모된 크레딧을 추적하세요.

모델별 제어

일반적인 기본값에 의존하지 말고 지원되는 생성 파라미터를 전달하세요.

하나의 계정과 잔액

같은 AIReiter 계정과 청구 시스템으로 지원되는 텍스트 모델을 테스트하고 운영하세요.

Claude Fable 5 FAQ

온라인 플레이그라운드, 요금, API 액세스에 대한 일반적인 질문입니다.

/ 01

Claude Fable 5는 무엇에 가장 적합한가요?

여기서는 장문 작성, 미묘한 재작성, 종합, 편집 수정에 적합한 도구로 제시됩니다.

/ 02

Claude Fable 5가 특정 글쓰기 스타일을 유지할 수 있나요?

대표적인 예시와 명확한 문체 제약을 제공한 다음, 플레이그라운드에서 수정본을 비교해 보세요.

/ 03

Claude Fable 5는 창작 글쓰기에만 사용되나요?

아니요. 보고서, 리서치 브리프, 문서화 및 기타 구조화된 장문 작업에도 평가할 수 있습니다.

/ 04

Claude Fable 5의 가격은 어떻게 되나요?

현재 입력 및 출력 토큰 요금은 플레이그라운드 위에 표시됩니다.

/ 05

API를 통해 Claude Fable 5를 사용할 수 있나요?

예. 연결된 API 문서를 따라 페이지의 모델 ID를 사용하세요.

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