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Claude Opus 5 AI 聊天 Playground 和 API

在线试用 Claude Opus 5,适用于复杂推理、要求高的编码工作和多步骤分析。查看实时 token 定价,并将同一模型接入你的 API 工作流。

输入官方 $5.00 每 100 万 TokensAIReiter $2.50 每 100 万 Tokens输出官方 $25.00 每 100 万 TokensAIReiter $12.50 每 100 万 Tokens缓存读取官方 $0.50 每 100 万 TokensAIReiter $0.25 每 100 万 Tokens缓存创建官方 $6.25 每 100 万 TokensAIReiter $3.13 每 100 万 Tokens
模型类型
使用 API 运行
Playground说明文档API

输入

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-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.

输出

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
输入 Token
134
输出 Token
2354
Tokens per second
55.13 tokens / second
Time to first token
-

模型详情

在 Playground、API 请求和内部工作流中使用相同的模型 key。

模型 ID
claude-opus-5
供应商
Anthropic
协议
Anthropic Messages
上下文窗口
1,000,000 Token
最大输出
128,000 Token
输入 Token
250 credits / 1M Token
输出 Token
1,250 credits / 1M Token
缓存读取
25 credits / 1M Token
缓存写入
312.5 credits / 1M Token

Claude Opus 5 可以做什么

当任务需要持续推理、谨慎决策,以及在多个相互依赖步骤中保持高质量输出时,选择 Claude Opus 5。

复杂推理

处理包含多重约束、相互竞争的证据以及相互依赖步骤的决策。

Agent 规划

起草计划、评估工具结果,并在更长的工作流中保持明确目标。

高风险代码审查

检查架构、追踪细微故障,并提出带有明确权衡的修改建议。

高管级综合

将密集的技术或商业材料转化为有说服力的建议和行动计划。

Claude Opus 5 适用场景

最适合处理代价高昂的错误、复杂依赖,以及最终决策质量比最快响应时间更重要的工作。
01

架构决策

在投入工程时间之前,对比迁移路径并识别风险。

02

复杂事件分析

将日志、代码和运行上下文中的证据关联起来。

03

Agent 协同编排

为多个工具或相互依赖的阶段规划并审查工作流。

04

战略研究

将密集材料综合为可被论证和辩护的决策。

如何使用 Claude Opus 5

通过三个简单步骤测试该模型。

01

选择你的设置

设置模型支持的响应控制和上传选项。

02

发送提示词

描述任务,添加相关上下文,并查看流式响应和 token 使用情况。

03

连接 API

使用文档中的端点和你的 API key,将同一模型接入你的产品。

使用 Claude Opus 5 API 构建

从交互式测试到生产集成,使用可预测的控制和用量报告顺利过渡。

熟悉的协议

使用为此模型配置的 API 协议,包括可用的流式传输。

用量可见性

在每次响应后跟踪输入 token、输出 token 和已消耗的积分。

模型专属控制

传入支持的生成参数,而不是依赖通用默认值。

一个账户和余额

通过同一个 AIReiter 账户和计费系统测试并使用受支持的文本模型。

Claude Opus 5 常见问题

关于在线 playground、价格和 API 访问的常见问题。

/ 01

我什么时候应该选择 Claude Opus 5?

适用于复杂的多步骤任务,在这些任务中,推理质量和谨慎决策足以匹配旗舰模型。

/ 02

Claude Opus 5 适合 coding agents 吗?

使用 playground 测试规划、代码审查和由工具驱动的工作流,针对你自己的仓库任务进行验证。

/ 03

Claude Opus 5 如何计费?

输入和输出 token 费率显示在 playground 上方,在投入生产路由之前应先确认。

/ 04

每个请求都应该使用 Claude Opus 5 吗?

不。将常规任务或对延迟敏感的工作路由到更轻量的模型,并将 Opus 5 留给更困难的请求。

/ 05

我可以通过 API 访问 Claude Opus 5 吗?

可以。使用此页面上的 API 文档,并发送显示的模型 ID。

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