AIREITER
API 文件價格
範本
AnthropicText Chat

Claude Opus 5 AI 聊天 Playground 和 API

線上試用 Claude Opus 5,適用於複雜推理、要求嚴苛的程式開發工作,以及多步驟分析。比較即時 token 價格,並將同一模型整合到您的 API 工作流程中。

输入官方 $5.00 每 100 萬 TokensAIReiter $2.50 每 100 萬 Tokens输出官方 $25.00 每 100 萬 TokensAIReiter $12.50 每 100 萬 Tokens缓存读取官方 $0.50 每 100 萬 TokensAIReiter $0.25 每 100 萬 Tokens缓存创建官方 $6.25 每 100 萬 TokensAIReiter $3.13 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
1
2
3
4
5
6
7
8
9
10
11
12
13

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-opus-5:

const message = await client.messages.create({
    "model": "claude-opus-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-opus-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-opus-5:

message = client.messages.create(
      model = "claude-opus-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-opus-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-opus-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-opus-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-opus-5",
  "input": {
    "model": "claude-opus-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-opus-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
250 credits / 1M Token
輸出 Token
1,250 credits / 1M Token
快取讀取
25 credits / 1M Token
快取寫入
312.5 credits / 1M Token

Claude Opus 5 能為您做什麼

當任務需要持續推理、審慎決策,以及跨多個相依步驟產出高品質結果時,請選擇 Claude Opus 5。

複雜推理

在多重限制、相互競爭的證據與相依步驟之間做出判斷。

代理規劃

擬定計畫、評估工具結果,並在較長的工作流程中維持清晰目標。

高風險程式碼審查

檢視架構、追蹤細微故障,並提出具體權衡的變更建議。

高層整合歸納

將密集的技術或商業資料轉化為有說服力的建議與行動方案。

Claude Opus 5 使用情境

最適合用於代價高昂的錯誤、複雜的相依關係,以及結果品質比最快回應時間更重要的工作。
01

架構決策

在投入工程時間前,比較遷移路徑並找出風險。

02

複雜事件分析

將日誌、程式碼與營運脈絡中的證據連結起來。

03

代理協調

為多種工具或相依階段規劃並審查工作流程。

04

策略研究

將密集資料整合成可被辯護的決策。

如何使用 Claude Opus 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 Opus 5 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。

Claude Opus 5 常見問題

關於線上 playground、價格與 API 存取的常見問題。

/ 01

我應該在什麼時候選擇 Claude Opus 5?

適用於需要複雜、多步驟處理的工作;其推理品質與審慎決策足以支撐旗艦模型的使用。

/ 02

Claude Opus 5 適合用於程式碼代理嗎?

使用 playground 針對您自己的 repository 任務測試規劃、程式碼審查與工具驅動的工作流程。

/ 03

Claude Opus 5 的價格如何計算?

輸入與輸出 token 費率會顯示在 playground 上方,並且在正式導入路由前應先確認。

/ 04

每個請求都應該使用 Claude Opus 5 嗎?

不用。將例行或對延遲敏感的工作交給較輕量的模型,並保留 Opus 5 處理較困難的請求。

/ 05

我可以透過 API 存取 Claude Opus 5 嗎?

可以。使用本頁連結的 API 文件,並傳送顯示的模型 ID。

AIREITER

有問題?請聯絡我們
[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 影片

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

AI 圖片

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

部落格

查看全部 →

公司

隱私政策服務條款退款政策

© 2026 AIReiter。保留所有權利。