Crun Chat Completions API

curl --request POST \
  --url https://api.crun.ai/api/v1/chat/completions \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "gpt-4o-mini",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "Write a short tagline for a coffee brand."
    }
  ],
  "temperature": 0.7
}
'

{
  "id": "chatcmpl_abc123",
  "object": "chat.completion",
  "created": 1772294400,
  "model": "gpt-4o-mini",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Bright flavor, smooth finish, and roasted for everyday focus."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 18,
    "completion_tokens": 13,
    "total_tokens": 31
  }
}

{
  "error": {
    "message": "Invalid JSON body",
    "type": "invalid_request_error",
    "param": null,
    "code": null
  }
}

{
  "error": {
    "message": "API key missing",
    "type": "authentication_error",
    "param": null,
    "code": "invalid_api_key"
  }
}

{
  "error": {
    "message": "Insufficient Credits",
    "type": "insufficient_quota",
    "param": null,
    "code": "insufficient_credits"
  }
}

{
  "error": {
    "message": "The model `unknown-model` does not exist.",
    "type": "invalid_request_error",
    "param": null,
    "code": "model_not_found"
  }
}

{
  "error": {
    "message": "Field `messages` is required.",
    "type": "invalid_request_error",
    "param": null,
    "code": null
  }
}

{
  "error": {
    "message": "Rate limit reached for this account, please retry later.",
    "type": "rate_limit_error",
    "param": null,
    "code": "rate_limit_exceeded"
  }
}

{
  "error": {
    "message": "Upstream request failed",
    "type": "api_error",
    "param": null,
    "code": null
  }
}

POST

api

chat

completions

curl --request POST \
  --url https://api.crun.ai/api/v1/chat/completions \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "gpt-4o-mini",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "Write a short tagline for a coffee brand."
    }
  ],
  "temperature": 0.7
}
'

{
  "id": "chatcmpl_abc123",
  "object": "chat.completion",
  "created": 1772294400,
  "model": "gpt-4o-mini",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Bright flavor, smooth finish, and roasted for everyday focus."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 18,
    "completion_tokens": 13,
    "total_tokens": 31
  }
}

{
  "error": {
    "message": "Invalid JSON body",
    "type": "invalid_request_error",
    "param": null,
    "code": null
  }
}

{
  "error": {
    "message": "API key missing",
    "type": "authentication_error",
    "param": null,
    "code": "invalid_api_key"
  }
}

{
  "error": {
    "message": "Insufficient Credits",
    "type": "insufficient_quota",
    "param": null,
    "code": "insufficient_credits"
  }
}

{
  "error": {
    "message": "The model `unknown-model` does not exist.",
    "type": "invalid_request_error",
    "param": null,
    "code": "model_not_found"
  }
}

{
  "error": {
    "message": "Field `messages` is required.",
    "type": "invalid_request_error",
    "param": null,
    "code": null
  }
}

{
  "error": {
    "message": "Rate limit reached for this account, please retry later.",
    "type": "rate_limit_error",
    "param": null,
    "code": "rate_limit_exceeded"
  }
}

{
  "error": {
    "message": "Upstream request failed",
    "type": "api_error",
    "param": null,
    "code": null
  }
}

API 地址

POST https://api.crun.ai/api/v1/chat/completions

该接口遵循 OpenAI Chat Completions 的请求和响应格式。您可以直接搭配官方 OpenAI SDK，或其他 OpenAI 兼容客户端使用。

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

请求示例

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 helpful assistant."
      },
      {
        "role": "user",
        "content": "Summarize the benefits of serverless functions in 3 bullet points."
      }
    ],
    "temperature": 0.6
  }'

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 helpful assistant."},
        {"role": "user", "content": "Summarize the benefits of serverless functions in 3 bullet points."},
    ],
    temperature=0.6,
)

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 helpful assistant." },
    { role: "user", content: "Summarize the benefits of serverless functions in 3 bullet points." }
  ],
  temperature: 0.6
});

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

对话历史示例

CRUN 不会为您的应用管理多轮对话上下文。如需继续一段对话，您必须自行维护历史消息，并在每次新请求中传入相关上下文。

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 helpful support assistant."
      },
      {
        "role": "user",
        "content": "My package has not arrived yet. What should I check first?"
      },
      {
        "role": "assistant",
        "content": "First, check the tracking link, delivery address, and any carrier delay notices."
      },
      {
        "role": "user",
        "content": "The tracking page says delayed by weather. Draft a short reply to the customer."
      }
    ],
    "temperature": 0.6
  }'

from openai import OpenAI

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

messages = [
    {"role": "system", "content": "You are a helpful support assistant."},
    {"role": "user", "content": "My package has not arrived yet. What should I check first?"},
    {"role": "assistant", "content": "First, check the tracking link, delivery address, and any carrier delay notices."},
    {"role": "user", "content": "The tracking page says delayed by weather. Draft a short reply to the customer."},
]

response = client.chat.completions.create(
    model="gpt-5.4",
    messages=messages,
    temperature=0.6,
)

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 messages = [
  { role: "system", content: "You are a helpful support assistant." },
  { role: "user", content: "My package has not arrived yet. What should I check first?" },
  { role: "assistant", content: "First, check the tracking link, delivery address, and any carrier delay notices." },
  { role: "user", content: "The tracking page says delayed by weather. Draft a short reply to the customer." }
];

const response = await client.chat.completions.create({
  model: "gpt-5.4",
  messages,
  temperature: 0.6
});

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

结构化输出示例

当您需要模型返回 JSON 时，可以使用 response_format。如果需要严格遵循 JSON Schema，请选择支持 json_schema 的模型和上游服务。

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": "Extract tasks as JSON only."
      },
      {
        "role": "user",
        "content": "Maya should send the launch brief by Friday."
      }
    ],
    "response_format": {
      "type": "json_schema",
      "json_schema": {
        "name": "task",
        "strict": true,
        "schema": {
          "type": "object",
          "properties": {
            "owner": { "type": "string" },
            "task": { "type": "string" },
            "due": { "type": "string" }
          },
          "required": ["owner", "task", "due"],
          "additionalProperties": false
        }
      }
    }
  }'

response = client.chat.completions.create(
    model="gpt-5.4",
    messages=[
        {"role": "system", "content": "Extract tasks as JSON only."},
        {"role": "user", "content": "Maya should send the launch brief by Friday."},
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "task",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {
                    "owner": {"type": "string"},
                    "task": {"type": "string"},
                    "due": {"type": "string"},
                },
                "required": ["owner", "task", "due"],
                "additionalProperties": False,
            },
        },
    },
)

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

const response = await client.chat.completions.create({
  model: "gpt-5.4",
  messages: [
    { role: "system", content: "Extract tasks as JSON only." },
    { role: "user", content: "Maya should send the launch brief by Friday." }
  ],
  response_format: {
    type: "json_schema",
    json_schema: {
      name: "task",
      strict: true,
      schema: {
        type: "object",
        properties: {
          owner: { type: "string" },
          task: { type: "string" },
          due: { type: "string" }
        },
        required: ["owner", "task", "due"],
        additionalProperties: false
      }
    }
  }
});

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

工具调用示例

Chat Completions 接受 OpenAI 兼容的 tools 与 tool_choice 字段。您的应用需要自行执行工具调用，并在后续请求中把工具结果传回模型。

request body

{
  "model": "gpt-5.4",
  "messages": [
    {
      "role": "user",
      "content": "What is the weather like in Paris?"
    }
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Get current weather for a city.",
        "parameters": {
          "type": "object",
          "properties": {
            "city": {
              "type": "string"
            }
          },
          "required": ["city"],
          "additionalProperties": false
        }
      }
    }
  ],
  "tool_choice": "auto"
}

视觉输入示例

当所选模型支持图像输入时，可以在用户消息中传入多模态 content parts。

request body

{
  "model": "gpt-5.4",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Describe this chart in one paragraph."
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://example.com/chart.png"
          }
        }
      ]
    }
  ],
  "max_completion_tokens": 300
}

流式请求示例

设置 stream=true 后，可以接收 Server-Sent Events。CRUN 会在可用时为流式请求启用 usage 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": "user",
        "content": "Write a three-step checklist for reviewing an API integration."
      }
    ],
    "stream_options": {
      "include_usage": true
    }
  }'

stream = client.chat.completions.create(
    model="gpt-5.4",
    messages=[
        {"role": "user", "content": "Write a three-step checklist for reviewing an API integration."}
    ],
    stream=True,
    stream_options={"include_usage": True},
)

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

const stream = await client.chat.completions.create({
  model: "gpt-5.4",
  messages: [
    { role: "user", content: "Write a three-step checklist for reviewing an API integration." }
  ],
  stream: true,
  stream_options: { include_usage: true }
});

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

响应示例

{
  "id": "chatcmpl_abc123",
  "object": "chat.completion",
  "created": 1772294400,
  "model": "gpt-5.4",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "- No server management.\n- Automatic scaling for bursty traffic.\n- Pay only for actual execution time."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 22,
    "completion_tokens": 19,
    "total_tokens": 41
  }
}

data: {"id":"chatcmpl_abc123","object":"chat.completion.chunk","created":1772294400,"model":"gpt-5.4","choices":[{"index":0,"delta":{"role":"assistant","content":"- No server management."},"finish_reason":null}]}

data: {"id":"chatcmpl_abc123","object":"chat.completion.chunk","created":1772294400,"model":"gpt-5.4","choices":[{"index":0,"delta":{"content":"\\n- Automatic scaling for bursty traffic."},"finish_reason":null}]}

data: {"id":"chatcmpl_abc123","object":"chat.completion.chunk","created":1772294400,"model":"gpt-5.4","choices":[],"usage":{"prompt_tokens":22,"completion_tokens":19,"total_tokens":41}}

data: [DONE]

注意事项

设置 stream=true 可接收 text/event-stream 响应。
如果省略 stream_options.include_usage，CRUN 会自动为流式请求启用该选项。
max_tokens 和 max_completion_tokens 都可以使用，并会受所选模型的输出 token 上限约束。
未知模型 ID 会返回 OpenAI 风格的错误体，其中 code 为 "model_not_found"。
对于新的有状态、工具密集或结构化输出工作流，也可以考虑 /responses 接口。Responses 使用 input、instructions 和 text.format，而不是 messages 和 response_format。

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。

LLM 快速开始

了解 base URL、身份验证方式以及官方 SDK 的接入模式。

模型概览

浏览所有可用的图像、视频、音频和语言模型 API。

价格

前往价格页面，比较不同模型的计费规则。

授权

Authorization

string

header

必填

在 OpenAI 兼容 SDK 中，将您的 CRUN API Key 作为 Bearer token 使用。

请求体

application/json

OpenAI 兼容的 Chat Completions 请求。其他兼容字段会被接受，并在模型支持时透传。

model

string

必填

由 GET /api/v1/models 返回的公开模型 ID。

Required string length: 1 - 128

示例:

"gpt-4o-mini"

messages

object[]

必填

OpenAI 格式的对话消息。

Minimum array length: 1

Show child attributes

temperature

number

采样温度。

必填范围: 0 <= x <= 2

示例:

0.7

top_p

number

核采样参数。

必填范围: 0 <= x <= 1

示例:

1

integer

默认值:1

要生成的补全结果数量。

必填范围: 1 <= x <= 8

示例:

1

stream

boolean

默认值:false

是否返回 Server-Sent Events 流式响应。

示例:

false

stop

停止序列或停止序列列表。

示例:

"###"

max_tokens

integer

最大输出 token 数，会受所选模型上限限制。

必填范围: x >= 1

示例:

512

max_completion_tokens

integer

最大补全 token 数，会受所选模型上限限制。

必填范围: x >= 1

示例:

512

presence_penalty

number

Presence penalty 参数。

必填范围: -2 <= x <= 2

示例:

0

frequency_penalty

number

Frequency penalty 参数。

必填范围: -2 <= x <= 2

示例:

0

user

string

用于可观测性的终端用户标识。

示例:

"user_123"

stream_options

object

流式输出选项。当 stream=true 且未传入该字段时，CRUN 默认启用 include_usage=true。

示例:

{ "include_usage": true }

response_format

object

OpenAI 兼容结构化输出选项。Chat Completions 使用 response_format；Responses 使用 text.format。

示例:

{ "type": "json_object" }

tools

object[]

OpenAI 兼容工具定义。应用需要执行工具调用，并在后续请求中返回工具结果。

tool_choice

工具选择策略。

示例:

"auto"

响应

成功的补全响应。stream=false 时返回 JSON，stream=true 时返回 SSE。

string

必填

示例:

"chatcmpl_abc123"

object

string

必填

示例:

"chat.completion"

created

integer

必填

Unix 时间戳，单位为秒。

示例:

1772294400

model

string

必填

客户端请求的公开模型 ID。

示例:

"gpt-4o-mini"

choices

object[]

必填

Show child attributes

usage

object

Show child attributes

Anthropic Messages Nano Banana 2

介绍

语言模型

图片模型

视频模型

语音模型

任务

Crun Chat Completions API

API 地址

身份验证

请求示例

对话历史示例

结构化输出示例

工具调用示例

视觉输入示例

流式请求示例

响应示例

注意事项

相关资源

Responses API

LLM 快速开始

模型概览

价格

授权

请求体

响应

​API 地址

​身份验证

​请求示例

​对话历史示例

​结构化输出示例

​工具调用示例

​视觉输入示例

​流式请求示例

​响应示例

​注意事项

​相关资源

Responses API

LLM 快速开始

模型概览

价格

授权

请求体

响应

API 地址

身份验证

请求示例

对话历史示例

结构化输出示例

工具调用示例

视觉输入示例

流式请求示例

响应示例

注意事项

相关资源