OpenAI 兼容语言模型

概览

CRUN 在 https://api.crun.ai/api/v1 下提供 OpenAI 和 Anthropic 兼容的语言模型网关。您只需要修改 base URL 和 API Key，就可以继续使用官方 OpenAI SDK，Anthropic SDK 或任何已经兼容 OpenAI 风格 Chat API 的客户端。

当前支持的兼容接口：

GET /models：列出当前可用的公开模型 ID
POST /responses：OpenAI最新的接口，推荐用于新的有状态、多模态和工具调用语言模型工作流
POST /chat/completions：面向现有聊天客户端的 OpenAI 兼容聊天生成接口
POST /messages：面向 Claude 风格消息客户端的 Anthropic 兼容接口
POST /completions：旧版文本补全接口；在当前兼容层中不支持流式输出

Base URL

https://api.crun.ai/api/v1

身份验证

您可以使用以下任一方式进行身份验证：

Authorization: Bearer YOUR_API_KEY
X-API-KEY: YOUR_API_KEY

如果使用官方 OpenAI SDK，推荐使用 Authorization: Bearer YOUR_API_KEY。

请妥善保管您的 API Key。切勿在客户端代码或公开代码仓库中暴露它。

第 1 步：列出可用模型

模型目录可能会随时间变化。集成或切换模型前，建议始终调用 /models 获取最新的公开模型 ID。

curl -X GET "https://api.crun.ai/api/v1/models" \
  -H "Authorization: Bearer YOUR_API_KEY"

import requests

response = requests.get(
    "https://api.crun.ai/api/v1/models",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    timeout=30,
)

print(response.json())

const response = await fetch("https://api.crun.ai/api/v1/models", {
  headers: {
    Authorization: "Bearer YOUR_API_KEY"
  }
});

console.log(await response.json());

响应示例

{
  "object": "list",
  "data": [
    {
      "id": "gpt-5.4",
      "object": "model",
      "created": 1772294400,
      "owned_by": "OpenAI"
    },
    {
      "id": "claude-sonnet-4-5",
      "object": "model",
      "created": 1772294400,
      "owned_by": "Anthropic"
    },
    {
      "id": "gemini-3.1-pro-preview",
      "object": "model",
      "created": 1772294400,
      "owned_by": "Google"
    }
  ]
}

第 2 步：发起第一次 Chat Completion 请求

如果您已有 OpenAI Chat Completions 客户端，或需要经典的 messages 与 choices 响应结构，可以使用 /chat/completions。该接口遵循 OpenAI Chat Completions 的请求和响应结构，因此现有 SDK 集成通常只需要替换 base URL。

curl -X POST "https://api.crun.ai/api/v1/chat/completions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.4",
    "messages": [
      {
        "role": "system",
        "content": "You are a concise assistant."
      },
      {
        "role": "user",
        "content": "Write a two-line product description for a wireless keyboard."
      }
    ],
    "temperature": 0.7
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.crun.ai/api/v1",
)

response = client.chat.completions.create(
    model="gpt-5.4",
    messages=[
        {"role": "system", "content": "You are a concise assistant."},
        {"role": "user", "content": "Write a two-line product description for a wireless keyboard."},
    ],
    temperature=0.7,
)

print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://api.crun.ai/api/v1"
});

const response = await client.chat.completions.create({
  model: "gpt-5.4",
  messages: [
    { role: "system", content: "You are a concise assistant." },
    { role: "user", content: "Write a two-line product description for a wireless keyboard." }
  ],
  temperature: 0.7
});

console.log(response.choices[0].message.content);

响应示例

{
  "id": "chatcmpl_abc123",
  "object": "chat.completion",
  "created": 1772294400,
  "model": "gpt-5.4",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Slim, quiet, and built for all-day comfort.\nA reliable wireless keyboard for focused work at home or on the go."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 24,
    "completion_tokens": 21,
    "total_tokens": 45
  }
}

第 3 步：使用 Responses API

新的集成建议优先考虑 /responses。它支持 OpenAI 更新的 Responses 形态，包括顶层 instructions、灵活的 input、用于有状态追问的 previous_response_id、用于结构化输出的 text.format，以及上游模型支持时可用的 tools。

curl -X POST "https://api.crun.ai/api/v1/responses" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.4",
    "instructions": "You are a concise assistant.",
    "input": "Write a two-line product description for a wireless keyboard.",
    "temperature": 0.7
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.crun.ai/api/v1",
)

response = client.responses.create(
    model="gpt-5.4",
    instructions="You are a concise assistant.",
    input="Write a two-line product description for a wireless keyboard.",
    temperature=0.7,
)

print(response.output_text)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://api.crun.ai/api/v1"
});

const response = await client.responses.create({
  model: "gpt-5.4",
  instructions: "You are a concise assistant.",
  input: "Write a two-line product description for a wireless keyboard.",
  temperature: 0.7
});

console.log(response.output_text);

响应示例

{
  "id": "resp_abc123",
  "object": "response",
  "created_at": 1772294400,
  "status": "completed",
  "model": "gpt-5.4",
  "output": [
    {
      "id": "msg_abc123",
      "type": "message",
      "role": "assistant",
      "content": [
        {
          "type": "output_text",
          "text": "Slim, quiet, and built for all-day comfort.\nA reliable wireless keyboard for focused work at home or on the go."
        }
      ]
    }
  ],
  "output_text": "Slim, quiet, and built for all-day comfort.\nA reliable wireless keyboard for focused work at home or on the go.",
  "usage": {
    "input_tokens": 20,
    "output_tokens": 21,
    "total_tokens": 41
  }
}

管理对话历史

CRUN 不会为您的应用存储或管理对话状态。如果希望模型记住之前的对话轮次，您的应用需要自行保存相关消息历史，并在每次新的 /chat/completions 请求中重新传入。

curl -X POST "https://api.crun.ai/api/v1/chat/completions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.4",
    "messages": [
      {
        "role": "system",
        "content": "You are a concise travel assistant."
      },
      {
        "role": "user",
        "content": "I will spend 2 days in Tokyo. Build a simple itinerary."
      },
      {
        "role": "assistant",
        "content": "Day 1: Asakusa and Ueno. Day 2: Shibuya and Meiji Shrine."
      },
      {
        "role": "user",
        "content": "Update it for rainy weather and add indoor options."
      }
    ],
    "temperature": 0.7
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.crun.ai/api/v1",
)

conversation_history = [
    {"role": "system", "content": "You are a concise travel assistant."},
    {"role": "user", "content": "I will spend 2 days in Tokyo. Build a simple itinerary."},
    {"role": "assistant", "content": "Day 1: Asakusa and Ueno. Day 2: Shibuya and Meiji Shrine."},
]

conversation_history.append(
    {"role": "user", "content": "Update it for rainy weather and add indoor options."}
)

response = client.chat.completions.create(
    model="gpt-5.4",
    messages=conversation_history,
    temperature=0.7,
)

print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://api.crun.ai/api/v1"
});

const conversationHistory = [
  { role: "system", content: "You are a concise travel assistant." },
  { role: "user", content: "I will spend 2 days in Tokyo. Build a simple itinerary." },
  { role: "assistant", content: "Day 1: Asakusa and Ueno. Day 2: Shibuya and Meiji Shrine." },
  { role: "user", content: "Update it for rainy weather and add indoor options." }
];

const response = await client.chat.completions.create({
  model: "gpt-5.4",
  messages: conversationHistory,
  temperature: 0.7
});

console.log(response.choices[0].message.content);

流式输出

设置 stream=true 后，可以从 /chat/completions 接收 Server-Sent Events。如果未提供 stream_options.include_usage，CRUN 会自动启用该选项，以便在流式响应接近结束时包含用量信息。

curl -N "https://api.crun.ai/api/v1/chat/completions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.4",
    "stream": true,
    "messages": [
        {"role": "system", "content": "You are a concise assistant."},
        {"role": "user", "content": "Write a two-line product description for a wireless keyboard."}
    ]
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.crun.ai/api/v1",
)

response = client.chat.completions.create(
    model="gpt-5.4",
    messages=[
        {"role": "system", "content": "You are a concise assistant."},
        {"role": "user", "content": "Write a two-line product description for a wireless keyboard."}
    ],
    stream=True,
    temperature=0.7,
)

for chunk in response:
    if chunk.choices:
        print(chunk.choices[0].delta.content, end="")

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://api.crun.ai/api/v1",
});

async function main() {
  const stream = await client.chat.completions.create({
    model: "gpt-5.4",
    messages: [
      { role: "system", content: "You are a concise assistant." },
      { role: "user", content: "Write a two-line product description for a wireless keyboard." }
    ],
    temperature: 0.7,
    stream: true,
  });

  for await (const chunk of stream) {
    const content = chunk.choices?.[0]?.delta?.content;
    if (content) {
      process.stdout.write(content);
    }
  }
}

main().catch(console.error);

响应示例

stream response

data: {"id":"","object":"chat.completion.chunk","created":0,"model":"gpt-5.4","choices":[{"index":0,"delta":{"role":"assistant","content":"S"}}]}

data: {"id":"","object":"chat.completion.chunk","created":0,"model":"gpt-5.4","choices":[{"index":0,"delta":{"role":"assistant","content":"le"}}]}

data: {"id":"resp_0c628e55cfc393b50069cddfec989c81939cd408ac45285daa","object":"chat.completion.chunk","created":1775099884,"model":"gpt-5.4","choices":[{"index":0,"delta":{"role":"assistant","content":""},"finish_reason":"stop"}],"usage":{"prompt_tokens":17,"completion_tokens":40,"total_tokens":57}}

data: [DONE]

注意事项

max_tokens 和 max_completion_tokens 都受支持。实际值会受所选模型的输出上限约束。
/responses 支持 max_output_tokens，实际值同样会受所选模型的输出上限约束。
其他 OpenAI 兼容字段也会被接受，并在上游模型支持时透传。
计费基于输入和输出 token 用量，并按具体模型设置最低计费值。最低值通常为 0.01。
新的有状态或工具调用工作流建议使用 /responses。现有聊天客户端仍可继续使用 /chat/completions。旧版 /completions 接口仅为兼容而提供，不支持 stream=true。

Responses API

使用灵活输入、有状态追问、结构化输出和工具就绪的 response items 构建应用。

Messages API

使用 Anthropic 兼容 messages、system prompt、工具调用、thinking 和流式事件。

Chat Completions API

查看请求参数、流式输出和 OpenAI 风格错误响应的完整 API 说明。

账户积分

在运行大批量语言模型任务前，查询账户剩余积分。

价格

前往价格页面，比较不同模型的计费规则。

介绍

语言模型

图片模型

视频模型

语音模型

任务

OpenAI 兼容语言模型

概览

Base URL

身份验证

第 1 步：列出可用模型

响应示例

第 2 步：发起第一次 Chat Completion 请求

响应示例

第 3 步：使用 Responses API

响应示例

管理对话历史

流式输出

响应示例

注意事项

相关资源

Responses API

Messages API

Chat Completions API

账户积分

价格

​概览

​Base URL

​身份验证

​第 1 步：列出可用模型

​响应示例

​第 2 步：发起第一次 Chat Completion 请求

​响应示例

​第 3 步：使用 Responses API

​响应示例

​管理对话历史

​流式输出

​响应示例

​注意事项

​相关资源

Responses API

Messages API

Chat Completions API

账户积分

价格

概览

Base URL

身份验证

第 1 步：列出可用模型

响应示例

第 2 步：发起第一次 Chat Completion 请求

响应示例

第 3 步：使用 Responses API

响应示例

管理对话历史

流式输出

响应示例

注意事项

相关资源