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Kimi K3 AI Chat Playground と API

OpenAI互換のChat Completions APIを通じて、長文コンテキストのコードベース、調査コレクション、ドキュメントレビュー、エージェントメモリ向けにKimi K3をオンラインで試しましょう。

入力公式 $3.00 100万トークンあたりAIReiter $1.50 100万トークンあたり出力公式 $15.00 100万トークンあたりAIReiter $7.50 100万トークンあたりキャッシュ読み取り公式 $0.30 100万トークンあたりAIReiter $0.15 100万トークンあたり
APIで実行
PlaygroundReadmeAPI

入力

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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.

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": "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
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
kimi-k3
プロバイダー
Moonshot
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
131,072 トークン
入力 Token
150 credits / 100万トークン
出力 Token
750 credits / 100万トークン
キャッシュ読み取り
15 credits / 100万トークン
キャッシュ書き込み
-

Kimi K3でできること

コンテキストがボトルネックで、1回のリクエストにコード、ドキュメント、証拠、またはエージェント履歴の大きな作業セットを含める必要がある場合にKimi K3を選びましょう。

長文コンテキストレビュー

大規模なコードベース、ドキュメントコレクション、または調査証拠を1つの作業コンテキストにまとめて保持します。

リポジトリ分析

ファイル間の関係を追跡し、より多くのプロジェクト状態を参照しながら変更について議論します。

調査の統合

構造化された結論を出す前に、多くのメモや情報源をまたいで主張を比較します。

エージェントメモリ評価

長いツールの実行履歴や過去の判断を確認し、自動化されたワークフローのどこで問題が起きたかを見つけます。

Kimi K3のユースケース

プロンプト内により多くの証拠を保持することで、早すぎるチャンク化、検索、またはプロジェクト状態の喪失を避けられるワークフローに最適です。
01

コードベースレビュー

1回のリクエストで、より多くのリポジトリコンテキストを分析します。

02

長文ドキュメントセット

契約書、ポリシー、レポート、または研究コレクションをレビューします。

03

エージェントトレース分析

長いツール履歴と保持された状態を確認します。

04

コンテキスト重視のプロトタイプ

検索機能を構築する前に、より多くのコンテキストで結果が改善するかをテストします。

Kimi K3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。

Kimi K3 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。

Kimi K3 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

Kimi K3は何に最適ですか?

1回のリクエストに、大規模なコード、ドキュメント、調査証拠、またはエージェント履歴の作業セットが必要な場合に使用します。

/ 02

Kimi K3で利用できるコンテキストウィンドウはどれですか?

AIReiterはKimi K3を1,048,576トークンのコンテキストウィンドウで掲載しています。非常に大きなリクエストを送信する前に、クライアントの制限とタイムアウトを確認してください。

/ 03

OpenAI形式のクライアントでKimi K3を呼び出せますか?

はい。AIReiterはOpenAI互換のChat Completionsエンドポイントを通じて提供しています。

/ 04

Kimi K3の価格はどのように設定されていますか?

現在の入力、キャッシュ読み取り、出力のトークン単価はAIReiterに表示されます。本番利用前に必ず確認してください。

/ 05

代わりに小さいモデルを選ぶべきなのはどんなときですか?

Kimi K3の長文コンテキスト能力が活かせない、短く状態を持たないリクエストには、より軽量なモデルを使用してください。

AIREITER

ご質問はお問い合わせください
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