AIREITER
API 문서가격
템플릿
AnthropicText Chat

Claude Opus 5 AI 채팅 플레이그라운드 및 API

복잡한 추론, 까다로운 코딩 작업, 다단계 분석을 위해 Claude Opus 5를 온라인에서 사용해 보세요. 실시간 토큰 가격을 비교하고 동일한 모델을 API 워크플로에 적용할 수 있습니다.

입력공식 $5.00 100만 토큰당AIReiter $2.50 100만 토큰당출력공식 $25.00 100만 토큰당AIReiter $12.50 100만 토큰당캐시 읽기공식 $0.50 100만 토큰당AIReiter $0.25 100만 토큰당캐시 생성공식 $6.25 100만 토큰당AIReiter $3.13 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
1
2
3
4
5
6
7
8
9
10
11
12
13

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-5:

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

message = client.messages.create(
      model = "claude-opus-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-opus-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-opus-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-opus-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-opus-5",
  "input": {
    "model": "claude-opus-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-opus-5
공급자
Anthropic
프로토콜
Anthropic Messages
컨텍스트 창
1,000,000 토큰
최대 출력
128,000 토큰
입력 Token
250 credits / 100만 토큰
출력 Token
1,250 credits / 100만 토큰
캐시 읽기
25 credits / 100만 토큰
캐시 쓰기
312.5 credits / 100만 토큰

Claude Opus 5로 할 수 있는 일

지속적인 추론, 신중한 판단, 여러 종속 단계에 걸친 고품질 결과가 필요한 작업에는 Claude Opus 5를 선택하세요.

복잡한 추론

여러 제약, 상충하는 근거, 종속적인 단계가 있는 의사결정을 처리합니다.

에이전트 계획

계획을 작성하고, 도구 결과를 평가하며, 더 긴 워크플로 전반에서 명확한 목표를 유지합니다.

고위험 코드 리뷰

아키텍처를 검토하고, 미묘한 실패를 추적하며, 명확한 트레이드오프와 함께 변경 사항을 제안합니다.

경영진용 종합 정리

밀도 높은 기술 또는 비즈니스 자료를 타당한 권고안과 실행 계획으로 전환합니다.

Claude Opus 5 활용 사례

비용이 큰 실수, 복잡한 종속성, 그리고 가장 짧은 응답 시간보다 최종 결정의 품질이 더 중요한 작업에 가장 적합합니다.
01

아키텍처 결정

엔지니어링 시간을 투입하기 전에 마이그레이션 경로를 비교하고 위험 요소를 드러냅니다.

02

복잡한 사고 분석

로그, 코드, 운영 맥락 전반의 증거를 연결합니다.

03

에이전트 오케스트레이션

여러 도구 또는 종속 단계가 있는 워크플로를 계획하고 검토합니다.

04

전략적 리서치

밀도 높은 자료를 방어 가능한 의사결정으로 종합합니다.

Claude Opus 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 Opus 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 Opus 5 FAQ

온라인 플레이그라운드, 요금, API 액세스에 대한 일반적인 질문입니다.

/ 01

Claude Opus 5는 언제 선택해야 하나요?

추론 품질과 신중한 판단이 플래그십 모델을 쓸 만한 복잡한 다단계 작업에 선택하세요.

/ 02

Claude Opus 5는 코딩 에이전트에 적합한가요?

플레이그라운드를 사용해 계획, 코드 리뷰, 도구 기반 워크플로를 실제 저장소 작업에 맞춰 테스트해 보세요.

/ 03

Claude Opus 5의 요금은 어떻게 되나요?

입력 및 출력 토큰 요금은 플레이그라운드 상단에 표시되며, 프로덕션 라우팅 전에 확인해야 합니다.

/ 04

모든 요청에 Claude Opus 5를 사용해야 하나요?

아니요. 일반적이거나 지연 시간에 민감한 작업은 더 가벼운 모델로 처리하고, 어려운 요청에는 Opus 5를 사용하세요.

/ 05

API를 통해 Claude Opus 5에 접근할 수 있나요?

네. 이 페이지에 연결된 API 문서를 사용하고 표시된 모델 ID를 보내세요.

AIREITER

문의가 있으신가요? 연락처
[email protected]

新速率有限公司NEWRATE LIMITED香港九龍花園街 2-16 號好景商業中心 2304 室Room 2304, Haojing Commercial Center, 2-16 Garden Street, Kowloon, Hong Kong

LLM

GPT-6 AstraGemini 3.8 FlashClaude Fable 5.1GLM-5.3 FlashGemini 3.6 Flash

AI 비디오

Gemini Omni 1.1 Flash ExtMiniMax H3Kling 3.0 Motion ControlKling 3.0 TurboKling 3.0

AI 이미지

GPT-Image 2.5Grok Imagine Image 2.0Midjourney V8.1Midjourney V7Z-Image Turbo

블로그

모두 보기 →

회사

개인정보 처리방침서비스 약관환불 정책

© 2026 AIReiter. All rights reserved.