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Kimi K3 AI 채팅 플레이그라운드 및 API

OpenAI 호환 Chat Completions API를 통해 긴 컨텍스트의 코드베이스, 연구 자료 모음, 문서 검토, 에이전트 메모리용 Kimi K3를 온라인에서 사용해 보세요.

입력공식 $3.00 100만 토큰당AIReiter $1.50 100만 토큰당출력공식 $15.00 100만 토큰당AIReiter $7.50 100만 토큰당캐시 읽기공식 $0.30 100만 토큰당AIReiter $0.15 100만 토큰당
API로 실행
플레이그라운드READMEAPI

입력

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Install the official OpenAI client — AIReiter speaks the same protocol, so only the base URL changes:

npm install openai

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.AIREITER_API_KEY,
  baseURL: "https://aireiter.com/api/v1",
});

Run kimi-k3:

const response = await client.chat.completions.create({
    "model": "kimi-k3",
    "messages": [
      {
        "role": "user",
        "content": "Send a message"
      }
    ],
    "max_tokens": 4096
  });

console.log(response);

Stream the response instead:

const stream = await client.chat.completions.create({
  ...{
    "model": "kimi-k3",
    "messages": [
      {
        "role": "user",
        "content": "Send a message"
      }
    ],
    "max_tokens": 4096
  },
  stream: true,
});

for await (const event of stream) {
  console.log(event);
}

Install the official OpenAI client — AIReiter speaks the same protocol, so only the base URL changes:

pip install openai

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIREITER_API_KEY"],
    base_url="https://aireiter.com/api/v1",
)

Run kimi-k3:

response = client.chat.completions.create(
      model = "kimi-k3",
      messages = [
        {
          role = "user",
          content = "Send a message"
        }
      ],
      max_tokens = 4096
)

print(response)

Stream the response instead:

stream = client.chat.completions.create(
      model = "kimi-k3",
      messages = [
        {
          role = "user",
          content = "Send a message"
        }
      ],
      max_tokens = 4096,
    stream=True,
)

for event in stream:
    print(event)

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Run kimi-k3 against AIReiter's API:

curl -s -X POST \
  -H "Authorization: Bearer $AIREITER_API_KEY" \
  -H "Content-Type: application/json" \
  "https://aireiter.com/api/v1/chat/completions" \
  -d '{
  "model": "kimi-k3",
  "messages": [
    {
      "role": "user",
      "content": "Send a message"
    }
  ],
  "max_tokens": 4096
}'

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": "kimi-k3",
  "input": {
    "model": "kimi-k3",
    "messages": [
      {
        "role": "user",
        "content": "Send a message"
      }
    ],
    "max_tokens": 4096
  },
  "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
kimi-k3
공급자
Moonshot
프로토콜
OpenAI Chat Completions
컨텍스트 창
1,048,576 토큰
최대 출력
131,072 토큰
입력 Token
150 credits / 100만 토큰
출력 Token
750 credits / 100만 토큰
캐시 읽기
15 credits / 100만 토큰
캐시 쓰기
-

Kimi K3로 할 수 있는 일

컨텍스트가 병목이 되고, 하나의 요청에 코드, 문서, 근거, 또는 에이전트 이력을 대규모로 담아야 할 때 Kimi K3를 선택하세요.

장문 컨텍스트 검토

대규모 코드베이스, 문서 모음 또는 연구 근거를 하나의 작업 컨텍스트에 함께 유지하세요.

리포지토리 분석

파일 간 관계를 추적하고 더 많은 프로젝트 상태를 바탕으로 변경 사항을 논의하세요.

연구 종합

구조화된 결론을 내리기 전에 여러 메모와 출처의 주장들을 비교하세요.

에이전트 메모리 평가

긴 도구 추적과 이전 결정을 검토하여 자동화 워크플로가 어디에서 잘못되었는지 찾으세요.

Kimi K3 사용 사례

프롬프트에 더 많은 근거를 보존함으로써 성급한 청킹, 검색 또는 프로젝트 상태 손실을 피할 수 있는 워크플로에 가장 적합합니다.
01

코드베이스 검토

하나의 요청으로 더 많은 리포지토리 컨텍스트를 분석하세요.

02

긴 문서 세트

계약서, 정책, 보고서 또는 연구 자료 모음을 검토하세요.

03

에이전트 추적 분석

긴 도구 기록과 유지된 상태를 점검하세요.

04

컨텍스트가 많은 프로토타입

검색 기능을 구축하기 전에 더 많은 컨텍스트가 결과를 개선하는지 테스트하세요.

Kimi K3 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.

Kimi K3 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.

Kimi K3 FAQ

온라인 플레이그라운드, 요금, API 액세스에 대한 일반적인 질문입니다.

/ 01

Kimi K3는 무엇에 가장 적합한가요?

요청에 대규모 코드, 문서, 연구 근거 또는 에이전트 이력이 필요할 때 사용하세요.

/ 02

Kimi K3에서 사용할 수 있는 컨텍스트 윈도우는 무엇인가요?

AIReiter는 Kimi K3의 컨텍스트 윈도우를 1,048,576 토큰으로 안내합니다. 매우 큰 요청을 보내기 전에 클라이언트 제한과 타임아웃을 확인하세요.

/ 03

OpenAI 스타일 클라이언트로 Kimi K3를 호출할 수 있나요?

네. AIReiter는 OpenAI 호환 Chat Completions 엔드포인트를 통해 이를 제공합니다.

/ 04

Kimi K3의 가격은 어떻게 책정되나요?

현재 입력, 캐시 읽기, 출력 토큰 요금은 AIReiter에 표시됩니다. 운영 환경에서 사용하기 전에 이를 확인하세요.

/ 05

대신 더 작은 모델을 선택해야 하는 경우는 언제인가요?

Kimi K3의 긴 컨텍스트 처리 용량이 도움이 되지 않는 짧고 상태를 유지하지 않는 요청에는 더 가벼운 모델을 사용하세요.

AIREITER

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