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Claude Sonnet 5 AI 聊天 Playground 與 API

線上試用 Claude Sonnet 5,體驗平衡的程式撰寫、分析、寫作與生產力助理功能。測試串流回應、查看 token 使用量,並整合 API。

输入官方 $2.00 每 100 萬 TokensAIReiter $1.00 每 100 萬 Tokens输出官方 $10.00 每 100 萬 TokensAIReiter $5.00 每 100 萬 Tokens缓存读取官方 $0.20 每 100 萬 TokensAIReiter $0.10 每 100 萬 Tokens缓存创建官方 $2.50 每 100 萬 TokensAIReiter $1.25 每 100 萬 Tokens
模型類型
使用 API 執行
PlaygroundREADMEAPI

輸入

imagefile[]
Optional input images sent alongside the prompt. Up to 5 files. Images are billed as input tokens.
Let the model reason before answering. The model decides how much thinking each request needs.Default: false
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Install the official Anthropic client — AIReiter speaks the same protocol, so only the base URL changes:

npm install @anthropic-ai/sdk

Set the AIREITER_API_KEY environment variable:

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

Point the client at AIReiter:

import Anthropic from "@anthropic-ai/sdk";

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

Run claude-sonnet-5:

const message = await client.messages.create({
    "model": "claude-sonnet-5",
    "max_tokens": 4096,
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "output_config": {
      "effort": "medium"
    }
  });

console.log(message.content);

Stream the response instead:

const stream = client.messages.stream({
    "model": "claude-sonnet-5",
    "max_tokens": 4096,
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "output_config": {
      "effort": "medium"
    }
  });

stream.on("text", (text) => process.stdout.write(text));
const message = await stream.finalMessage();

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

pip install anthropic

Set the AIREITER_API_KEY environment variable:

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

Point the client at AIReiter:

import os
import anthropic

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

Run claude-sonnet-5:

message = client.messages.create(
      model = "claude-sonnet-5",
      max_tokens = 4096,
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      output_config = {
        effort = "medium"
      }
)

print(message.content)

Stream the response instead:

with client.messages.stream(
      model = "claude-sonnet-5",
      max_tokens = 4096,
      messages = [
        {
          role = "user",
          content = "Explain what an API rate limit is and how to handle a 429 response in code."
        }
      ],
      output_config = {
        effort = "medium"
      }
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Set the AIREITER_API_KEY environment variable:

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

Run claude-sonnet-5 against AIReiter's API:

curl -s -X POST \
  -H "x-api-key: $AIREITER_API_KEY" \
  -H "Content-Type: application/json" \
  "https://aireiter.com/api/v1/messages" \
  -d '{
  "model": "claude-sonnet-5",
  "max_tokens": 4096,
  "messages": [
    {
      "role": "user",
      "content": "Explain what an API rate limit is and how to handle a 429 response in code."
    }
  ],
  "output_config": {
    "effort": "medium"
  }
}'

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": "claude-sonnet-5",
  "input": {
    "model": "claude-sonnet-5",
    "max_tokens": 4096,
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API rate limit is and how to handle a 429 response in code."
      }
    ],
    "output_config": {
      "effort": "medium"
    }
  },
  "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
claude-sonnet-5
供應商
Anthropic
協定
Anthropic Messages
上下文視窗
1,000,000 Token
最大輸出
128,000 Token
輸入 Token
100 credits / 1M Token
輸出 Token
500 credits / 1M Token
快取讀取
10 credits / 1M Token
快取寫入
125 credits / 1M Token

Claude Sonnet 5 能為你做什麼

選擇 Claude Sonnet 5 作為平衡的生產模型,適合需要穩定日常品質、又不想讓每個請求都走最高成本級別的團隊。

生產級程式撰寫

在日常開發中實作功能、解釋不熟悉的程式碼,並反覆迭代修正。

平衡分析

比較選項並總結證據,而不需要只使用旗艦級工作流程的額外負擔。

客戶助理

驅動實用的支援與內部助理,提供清楚且有條理的回應。

結構化內容

產出規格、簡報、版本更新說明與可重用的營運文件。

Claude Sonnet 5 使用情境

最適合生產工作中廣泛的中間層需求:能力足以應付嚴謹任務,也足夠務實可反覆使用。
01

產品開發

在程式碼、測試、文件與實作決策之間切換。

02

內部知識助理

以清楚且有用的格式回答營運相關問題。

03

支援自動化

撰寫準確回覆,並將模糊案例升級處理。

04

內容產製

為日常商務需求建立結構化、可重用的草稿。

如何使用 Claude Sonnet 5

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 Claude Sonnet 5 API 開發

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。

Claude Sonnet 5 常見問題

關於線上 playground、價格與 API 存取的常見問題。

/ 01

我什麼時候該選擇 Claude Sonnet 5?

把它作為平衡的生產模型,用於程式撰寫、分析、助理與結構化寫作。

/ 02

Sonnet 5 與 Opus 等級有什麼不同?

當你需要穩定的日常能力,又不想把每項任務都路由到旗艦等級時,Sonnet 是實用的預設選擇。

/ 03

Claude Sonnet 5 可以串流回應嗎?

可以。playground 會串流輸出,並在完成後回報使用量。

/ 04

Claude Sonnet 5 如何計價?

目前的輸入與輸出 token 費率顯示在 playground 上方。

/ 05

我可以透過 API 整合 Claude Sonnet 5 嗎?

可以。開啟連結的 API 文件並傳送顯示的模型 ID。

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