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Claude Sonnet 5 AI Chat Playground 및 API

균형 잡힌 코딩, 분석, 글쓰기, 프로덕션용 어시스턴트를 위해 Claude Sonnet 5를 온라인에서 사용해 보세요. 스트리밍 응답을 테스트하고, 토큰 사용량을 확인하며, API를 연동할 수 있습니다.

입력공식 $2.00 100만 토큰당AIReiter $1.00 100만 토큰당출력공식 $10.00 100만 토큰당AIReiter $5.00 100만 토큰당캐시 읽기공식 $0.20 100만 토큰당AIReiter $0.10 100만 토큰당캐시 생성공식 $2.50 100만 토큰당AIReiter $1.25 100만 토큰당
모델 유형
API로 실행
플레이그라운드READMEAPI

입력

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-sonnet-5:

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

message = client.messages.create(
      model = "claude-sonnet-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-sonnet-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-sonnet-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-sonnet-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-sonnet-5",
  "input": {
    "model": "claude-sonnet-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-sonnet-5
공급자
Anthropic
프로토콜
Anthropic Messages
컨텍스트 창
1,000,000 토큰
최대 출력
128,000 토큰
입력 Token
100 credits / 100만 토큰
출력 Token
500 credits / 100만 토큰
캐시 읽기
10 credits / 100만 토큰
캐시 쓰기
125 credits / 100만 토큰

Claude Sonnet 5로 할 수 있는 일

모든 요청을 최고 비용 등급으로 보내지 않아도 되는, 강력한 일상 품질이 필요한 팀을 위한 균형 잡힌 프로덕션 모델로 Claude Sonnet 5를 선택하세요.

프로덕션 코딩

일상적인 개발 과정에서 기능을 구현하고, 익숙하지 않은 코드를 설명하며, 수정 사항을 반복적으로 개선하세요.

균형 잡힌 분석

최고 사양 전용 워크플로의 부담 없이 옵션을 비교하고 근거를 요약하세요.

고객 지원 어시스턴트

명확하고 잘 구조화된 응답이 필요한 유용한 지원 및 내부 어시스턴트를 구현하세요.

구조화된 콘텐츠

사양서, 브리프, 릴리스 노트, 재사용 가능한 운영 문서를 작성하세요.

Claude Sonnet 5 활용 사례

프로덕션 업무의 넓은 중간 영역에 가장 적합합니다. 중요한 작업을 처리할 만큼 유능하면서도 반복적으로 사용하기에 실용적입니다.
01

제품 개발

코드, 테스트, 문서, 구현 결정을 오가며 작업하세요.

02

내부 지식 어시스턴트

운영 관련 질문에 명확하고 유용한 형식으로 답변하세요.

03

지원 자동화

정확한 응답 초안을 작성하고 모호한 사례는 상급 단계로 이관하세요.

04

콘텐츠 제작

일상적인 비즈니스 요구에 맞는 구조화되고 재사용 가능한 초안을 작성하세요.

Claude Sonnet 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 Sonnet 5 API로 개발하기

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 Sonnet 5 FAQ

온라인 플레이그라운드, 요금, API 액세스에 대한 일반적인 질문입니다.

/ 01

언제 Claude Sonnet 5를 선택해야 하나요?

코딩, 분석, 어시스턴트, 구조화된 글쓰기를 위한 균형 잡힌 프로덕션 모델로 선택하세요.

/ 02

Sonnet 5는 Opus 등급과 어떻게 다른가요?

모든 작업을 플래그십 등급으로 보내지 않고도 강력한 일상 성능이 필요할 때 Sonnet이 실용적인 기본 선택입니다.

/ 03

Claude Sonnet 5는 응답을 스트리밍할 수 있나요?

네. 플레이그라운드는 출력을 스트리밍하고 완료 후 사용량을 보고합니다.

/ 04

Claude Sonnet 5의 요금은 어떻게 책정되나요?

현재 입력 및 출력 토큰 요금은 플레이그라운드 위에 표시됩니다.

/ 05

API를 통해 Claude Sonnet 5를 통합할 수 있나요?

네. 연결된 API 문서를 열고 표시된 모델 ID를 보내세요.

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