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MoonshotText Chat

Kimi K3 AI 聊天 Playground 和 API

透過相容於 OpenAI 的 Chat Completions API,線上試用 Kimi K3,適用於長上下文程式碼庫、研究資料集、文件審閱與代理記憶。

输入官方 $3.00 每 100 萬 TokensAIReiter $1.50 每 100 萬 Tokens输出官方 $15.00 每 100 萬 TokensAIReiter $7.50 每 100 萬 Tokens缓存读取官方 $0.30 每 100 萬 TokensAIReiter $0.15 每 100 萬 Tokens
使用 API 執行
PlaygroundREADMEAPI

輸入

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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.

輸出

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
輸入 Token
134
輸出 Token
2354
Tokens per second
55.13 tokens / second
Time to first token
-

模型詳情

在 Playground、API 請求與內部工作流程中使用相同的模型鍵。

模型 ID
kimi-k3
供應商
Moonshot
協定
OpenAI Chat Completions
上下文視窗
1,048,576 Token
最大輸出
131,072 Token
輸入 Token
150 credits / 1M Token
輸出 Token
750 credits / 1M Token
快取讀取
15 credits / 1M Token
快取寫入
-

使用 Kimi K3 可以做什麼

當上下文是瓶頸,且單次請求需要承載大量程式碼、文件、證據或代理歷史時,選擇 Kimi K3。

長上下文審閱

將大型程式碼庫、文件集合或研究證據整合在同一個工作上下文中。

儲存庫分析

在可取得更多專案狀態的情況下,追蹤檔案之間的關係並討論變更。

研究整合

在產出結構化結論之前,比對多份筆記與來源中的主張。

代理記憶評估

檢視長篇工具追蹤與先前決策,找出自動化工作流程出錯的地方。

Kimi K3 使用情境

最適合那些在提示中保留更多證據,能避免過早分塊、檢索或遺失專案狀態的工作流程。
01

程式碼庫審閱

在單一請求中分析更多儲存庫上下文。

02

長文件集合

審閱合約、政策、報告或研究資料集。

03

代理追蹤分析

檢視長篇工具歷史與保留狀態。

04

高上下文原型

在建立即時檢索之前,先測試更多上下文是否能改善結果。

如何使用 Kimi K3

透過三個簡單步驟測試這個模型。

01

選擇設定

設定模型支援的回應控制與上傳選項。

02

送出提示

描述任務、加入相關脈絡,並查看串流回應與 token 使用量。

03

連接 API

使用文件中的端點與你的 API key,將同一模型整合到你的產品中。

使用 Kimi K3 API 進行建置

從互動式測試一路進階到正式整合,並享有可預測的控制與用量報告。

熟悉的協議

使用此模型設定的 API 協議,包括可用時的串流。

用量可視化

在每次回應後追蹤輸入 token、輸出 token 和已消耗的點數。

模型專屬控制

傳入支援的生成參數,而不是依賴通用預設值。

一個帳戶與餘額

透過相同的 AIReiter 帳戶與計費系統測試並操作支援的文字模型。

Kimi K3 常見問題

關於線上 playground、價格與 API 存取的常見問題。

/ 01

Kimi K3 最適合做什麼?

當請求需要大量的程式碼、文件、研究證據或代理歷史作為工作上下文時,請使用它。

/ 02

Kimi K3 可用的上下文視窗是多少?

AIReiter 將 Kimi K3 標示為 1,048,576 token 的上下文視窗;在送出超大型請求前,請先確認用戶端限制與逾時設定。

/ 03

我可以使用 OpenAI 風格的用戶端呼叫 Kimi K3 嗎?

可以。AIReiter 透過相容於 OpenAI 的 Chat Completions 端點提供此功能。

/ 04

Kimi K3 如何計價?

目前的輸入、快取讀取和輸出 token 費率由 AIReiter 顯示;在正式使用前請先確認。

/ 05

什麼時候我應該改選較小的模型?

對於短篇、無狀態且無法受益於 Kimi K3 長上下文能力的請求,請使用較輕量的模型。

AIREITER

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