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Gemini 3.6 Flash AI 채팅 플레이그라운드 및 API

반응형 어시스턴트, 빠른 콘텐츠 처리, 스트리밍 출력과 표시되는 토큰 사용량을 갖춘 대량 API 워크플로우를 위해 Gemini 3.6 Flash를 온라인으로 사용해 보세요.

입력공식 $0.75 100만 토큰당AIReiter $0.23 100만 토큰당출력공식 $3.75 100만 토큰당AIReiter $1.13 100만 토큰당캐시 읽기공식 $0.07 100만 토큰당AIReiter $0.02 100만 토큰당
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 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 gemini-3.6-flash:

const response = await client.chat.completions.create({
    "model": "gemini-3.6-flash",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "temperature": 1,
    "top_p": 1
  });

console.log(response);

Stream the response instead:

const stream = await client.chat.completions.create({
  ...{
    "model": "gemini-3.6-flash",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "temperature": 1,
    "top_p": 1
  },
  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 gemini-3.6-flash:

response = client.chat.completions.create(
      model = "gemini-3.6-flash",
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      max_tokens = 4096,
      temperature = 1,
      top_p = 1
)

print(response)

Stream the response instead:

stream = client.chat.completions.create(
      model = "gemini-3.6-flash",
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      max_tokens = 4096,
      temperature = 1,
      top_p = 1,
    stream=True,
)

for event in stream:
    print(event)

Set the AIREITER_API_KEY environment variable:

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

Run gemini-3.6-flash 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": "gemini-3.6-flash",
  "messages": [
    {
      "role": "user",
      "content": "Explain what an API rate limit is and how to handle a 429 response in code."
    }
  ],
  "max_tokens": 4096,
  "temperature": 1,
  "top_p": 1
}'

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": "gemini-3.6-flash",
  "input": {
    "model": "gemini-3.6-flash",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "max_tokens": 4096,
    "temperature": 1,
    "top_p": 1
  },
  "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
gemini-3.6-flash
공급자
Google
프로토콜
OpenAI Chat Completions
컨텍스트 창
1,048,576 토큰
최대 출력
65,536 토큰
입력 Token
22.5 credits / 100만 토큰
출력 Token
112.5 credits / 100만 토큰
캐시 읽기
2.25 credits / 100만 토큰
캐시 쓰기
-

Gemini 3.6 Flash로 할 수 있는 일

빠른 답변과 많은 요청에 걸친 안정적인 처리량이 필요한 반응형 제품에는 Gemini 3.6 Flash를 선택하세요.

반응형 어시스턴트

스트리밍 응답으로 대화형 채팅과 생산성 경험을 원활하게 유지하세요.

신속한 문서 처리

유입되는 텍스트를 프로덕션 속도로 요약, 변환, 추출하세요.

콘텐츠 운영

대규모 대기열 전반에서 변형, 메타데이터, 개요, 구조화된 초안을 생성하세요.

API 자동화

예측 가능한 처리량이 답변 품질만큼 중요한 빈번한 텍스트 작업을 실행하세요.

Gemini 3.6 Flash 활용 사례

더 강력한 Flash급 모델이 필요한 대화형 및 고처리량 제품에 가장 적합합니다.
01

대화형 채팅

반복되는 사용자 턴 동안 어시스턴트의 반응성을 유지하세요.

02

문서 파이프라인

유입되는 콘텐츠를 빠르게 요약하고 변환하세요.

03

마케팅 운영

대규모 배치 전반에서 변형, 태그, 브리프를 생성하세요.

04

자동화 백엔드

예측 가능한 처리량으로 반복적인 텍스트 작업을 처리하세요.

Gemini 3.6 Flash 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.

Gemini 3.6 Flash API로 빌드하기

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.

Gemini 3.6 Flash FAQ

온라인 플레이그라운드, 요금, API 액세스에 대한 일반적인 질문입니다.

/ 01

Gemini 3.6 Flash는 무엇에 가장 적합한가요?

반응형 어시스턴트, 빠른 문서 처리, 콘텐츠 운영, 빈번한 API 작업에 사용하세요.

/ 02

Gemini 3.6 Flash는 어떻게 평가해야 하나요?

고처리량 프로덕션 트래픽을 할당하기 전에 응답 품질과 지연 시간 모두에 대해 대표적인 프롬프트를 테스트하세요.

/ 03

Gemini 3.6 Flash는 응답을 스트리밍하나요?

네. 플레이그라운드에서는 생성되는 대로 스트리밍된 출력이 표시됩니다.

/ 04

Gemini 3.6 Flash의 가격은 어떻게 책정되나요?

현재 입력 및 출력 토큰 요금은 플레이그라운드 위에 표시됩니다.

/ 05

API를 통해 Gemini 3.6 Flash에 액세스할 수 있나요?

예. 연결된 API 문서와 페이지 모델 ID를 사용하세요.

AIREITER

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