AIREITER
APIドキュメント料金
テンプレート
GoogleText Chat

Gemini 3.6 Flash AI Chat Playground と API

応答性の高いアシスタント、迅速なコンテンツ処理、大規模なAPIワークフロー向けに、ストリーミング出力と可視化されたトークン使用量を備えた Gemini 3.6 Flash をオンラインで試してみましょう。

入力公式 $0.75 100万トークンあたりAIReiter $0.23 100万トークンあたり出力公式 $3.75 100万トークンあたりAIReiter $1.13 100万トークンあたりキャッシュ読み取り公式 $0.07 100万トークンあたりAIReiter $0.02 100万トークンあたり
APIで実行
PlaygroundReadmeAPI

入力

imagefile[]
Optional input images sent alongside the prompt. Up to 5 files. Images are billed as input tokens.
1
2
3
4
5
6
7
8
9
10
11
12

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.

出力

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
入力 Token
134
出力 Token
2354
Tokens per second
55.13 tokens / second
Time to first token
-

モデル詳細

Playground、APIリクエスト、社内ワークフローで同じモデルキーを使用してください。

モデル ID
gemini-3.6-flash
プロバイダー
Google
プロトコル
OpenAI Chat Completions
コンテキストウィンドウ
1,048,576 トークン
最大出力
65,536 トークン
入力 Token
22.5 credits / 100万トークン
出力 Token
112.5 credits / 100万トークン
キャッシュ読み取り
2.25 credits / 100万トークン
キャッシュ書き込み
-

Gemini 3.6 Flash でできること

複数のリクエストにわたって、素早い応答と信頼できるスループットが必要な応答性の高い製品には、Gemini 3.6 Flash をお選びください。

応答性の高いアシスタント

ストリーミング応答で、対話型チャットや生産性向上の体験をスムーズに進められます。

迅速なドキュメント処理

受信したテキストを本番速度で要約、変換、抽出します。

コンテンツ運用

大規模なキュー全体で、バリエーション、メタデータ、アウトライン、構造化ドラフトを生成します。

API自動化

予測可能なスループットが回答品質と同じくらい重要な、高頻度のテキストタスクを実行します。

Gemini 3.6 Flash のユースケース

より強力な Flash ティアモデルを必要とする、対話型かつ高スループットの製品に最適です。
01

対話型チャット

繰り返しのユーザーターンでもアシスタントの応答性を維持します。

02

ドキュメントパイプライン

受信コンテンツをすばやく要約し、変換します。

03

マーケティング運用

大規模なバッチ全体で、バリエーション、タグ、ブリーフを作成します。

04

自動化バックエンド

予測可能なスループットで、定常的なテキストジョブを処理します。

Gemini 3.6 Flash の使い方

3 つの簡単なステップでモデルを試せます。

01

設定を選ぶ

モデルがサポートする応答コントロールとアップロードオプションを設定します。

02

プロンプトを送信

タスクを説明し、関連するコンテキストを追加して、ストリーミング応答とトークン使用量を確認します。

03

API を接続

ドキュメント化されたエンドポイントと API key を使って、同じモデルをあなたの製品に組み込みます。

Gemini 3.6 Flash API で構築する

予測可能な制御と使用状況レポートを備えたインタラクティブなテストから、本番統合へ進めます。

馴染みのあるプロトコル

このモデル用に設定されたAPIプロトコルを使用します。利用可能な場合はストリーミングも含まれます。

使用状況の可視化

各応答後に、入力トークン、出力トークン、および消費クレジットを追跡できます。

モデル固有のコントロール

汎用のデフォルトに頼らず、対応している生成パラメータを渡してください。

1つのアカウントと残高

同じAIReiterアカウントと請求システムで、対応しているテキストモデルをテストし、運用できます。

Gemini 3.6 Flash FAQ

オンラインplayground、料金、APIアクセスに関するよくある質問。

/ 01

Gemini 3.6 Flash は何に最適ですか?

応答性の高いアシスタント、迅速なドキュメント処理、コンテンツ運用、頻繁なAPIジョブに使用してください。

/ 02

Gemini 3.6 Flash はどのように評価すべきですか?

大量の本番トラフィックを割り当てる前に、代表的なプロンプトで応答品質とレイテンシの両方をテストしてください。

/ 03

Gemini 3.6 Flash は応答をストリーミングしますか?

はい。Playground では、生成されると同時にストリーミング出力が表示されます。

/ 04

Gemini 3.6 Flashの料金はどのようになっていますか?

現在の入力トークンと出力トークンの料金はplaygroundの上部に表示されています。

/ 05

Gemini 3.6 FlashにはAPI経由でアクセスできますか?

はい。リンク先のAPIドキュメントとページのモデルIDをご利用ください。

AIREITER

ご質問はお問い合わせください
[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動画

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

AI画像

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

ブログ

すべて表示 →

会社

プライバシーポリシー利用規約返金ポリシー

© 2026 AIReiter. All rights reserved.