AiHubMix Documentation Hub

支持 Openai 的 Responses API 多功能接口，已经上线的功能接口如下：

Text input：文本输入
Image input：图文输入
Streaming：流式调用
Web search：搜索
Deep research：深度研究
Reasoning：推理深度控制，支持 4 档 (minimal / low / medium /high)，其中，minimal 仅适用于 gpt-5 系列，completion 端口中参数名为 reasoning_effort
Verbosity：输出篇幅，gpt-5 系列支持 3 档 (low / medium / high)，其中，gpt-5-chat 仅支持 medium，completions 端口需要更新 openai 包来支持此参数
Functions：函数调用
image_generation：绘图工具调用，图片生成部分按 gpt-image-1 计价
Code Interpreter：代码解析器，与 gpt-5 搭配时，不支持 reasoning.effort ‘minimal’ 档位
Remote MCP：MCP 调用
Computer Use：自动操作

使用 (Python 调用)：

与官方的 OpenAI 调用方式一致，只是替换 api_key 和 base_url 进行转发。大陆可以直连访问。

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://aihubmix.com/v1"
)

对于推理模型，支持通过以下参数来输出推理总结，总结细节的丰富程度为 detailed > auto > None，其中 auto 为最佳平衡。

"summary": "auto"

gpt-5-chat 在不传入 reasoning.effort 的情况下，相当于关闭推理，适用于会话场景。
深度研究模型可选：o3-deep-research 和 o4-mini-deep-research，仅支持 responses 端口
gpt-5 系列强调稳定推理和一致性输出，不再支持用于控制随机性的 temprature 和 top_p 参数，如果你需要更多自由度，可以尝试支持 temprature 的 gpt-5-chat-latest
推理模型（o 系列 / gpt-5 系列）已废弃 ‎max_tokens，请使用 completion 的 ‎max_completion_tokens 或 responses 的 max_output_tokens 明确限定输出 token 上限。

from openai import OpenAI

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://aihubmix.com/v1"
)

response = client.responses.create(
    model="gpt-5", # gpt-5, gpt-5-chat-latest, gpt-5-mini, gpt-5-nano
    input="Why does tarot reading work, what are the underlying principles, and what transferable methods are there? Output format: Markdown", # GPT-5 默认不使用 Markdown 格式输出，需要明确指定。
    reasoning={
        "effort": "minimal" # 推理深度 - Controls how many reasoning tokens the model generates before producing a response. value can be "minimal", "low", "medium", "high", default is "medium"
    },
    text={
        "verbosity": "low" # 输出篇幅 - Verbosity determines how many output tokens are generated. value can be "low", "medium", "high", Models before GPT-5 have used medium verbosity by default. 
    },
    stream=True
)

for event in response:
  print(event)

from openai import OpenAI
import os

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://aihubmix.com/v1"
)

response = client.responses.create(
  model="gpt-4o-mini", # codex-mini-latest 可用
  input="Tell me a three sentence bedtime story about a unicorn."
)

print(response)

from openai import OpenAI

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://aihubmix.com/v1"
)

response = client.responses.create(
    model="gpt-4o-mini", # codex-mini-latest 可用
    input=[
        {
            "role": "user",
            "content": [
                { "type": "input_text", "text": "what is in this image?" },
                {
                    "type": "input_image",
                    "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
                }
            ]
        }
    ]
)

print(response)

from openai import OpenAI

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://aihubmix.com/v1"
)

response = client.responses.create(
  model="gpt-4o-mini", # codex-mini-latest 可用
  instructions="You are a helpful assistant.",
  input="Hello!",
  stream=True
)

for event in response:
  print(event)

from openai import OpenAI

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://aihubmix.com/v1"
)

response = client.responses.create(
  model="gpt-4o-mini", # codex-mini-latest 不支持搜索📍
  tools=[{ "type": "web_search_preview" }],
  input="What was a positive news story from today?",
)

print(response)

from openai import OpenAI

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://aihubmix.com/v1",
    timeout=3600
)

input_text = """
Research the economic impact of semaglutide on global healthcare systems.
Do:
- Include specific figures, trends, statistics, and measurable outcomes.
- Prioritize reliable, up-to-date sources: peer-reviewed research, health
  organizations (e.g., WHO, CDC), regulatory agencies, or pharmaceutical
  earnings reports.
- Include inline citations and return all source metadata.

Be analytical, avoid generalities, and ensure that each section supports
data-backed reasoning that could inform healthcare policy or financial modeling.
"""

response = client.responses.create(
  model="o3-deep-research", # o4-mini-deep-research
  input=input_text,
  tools=[
    {"type": "web_search_preview"},
    {"type": "code_interpreter", "container": {"type": "auto"}},
  ],
)

print(response.output_text)

from openai import OpenAI

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://aihubmix.com/v1/"
)

response = client.responses.create(
    model="o4-mini", # 支持 codex-mini-latest, o4-mini, o3-mini, o3, o1
    input="How much wood would a woodchuck chuck?",
    reasoning={
        "effort": "medium", # 支持 low, medium, high
        "summary": "auto" # 推理总结
    }
)

print(response)

from openai import OpenAI

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://aihubmix.com/v1"
)

tools = [
    {
        "type": "function",
        "name": "get_current_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
          "type": "object",
          "properties": {
              "location": {
                  "type": "string",
                  "description": "The city and state, e.g. San Francisco, CA",
              },
              "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
          },
          "required": ["location", "unit"],
        }
    }
]

response = client.responses.create(
  model="gpt-4o-mini", # codex-mini-latest 可用
  tools=tools,
  input="What is the weather like in Boston today?",
  tool_choice="auto"
)

print(response)

from openai import OpenAI
import base64

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://aihubmix.com/v1"
)

response = client.responses.create(
    model="gpt-4.1-mini",
    input="Generate an image of gray tabby cat hugging an otter with an orange scarf",
    tools=[
        {
            "type": "image_generation",
            "background": "opaque", 
            "quality": "high",
        }
    ],
)

# 保存为图片文件
image_data = [
    output.result
    for output in response.output
    if output.type == "image_generation_call"
]

if image_data:
    image_base64 = image_data[0]
    with open("cat_and_otter.png", "wb") as f:
        f.write(base64.b64decode(image_base64))

from openai import OpenAI

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://aihubmix.com/v1"
)

instructions = """
You are a personal math tutor. When asked a math question, 
write and run code using the python tool to answer the question.
"""

resp = client.responses.create(
    model="gpt-4.1",
    tools=[
        {
            "type": "code_interpreter",
            "container": {"type": "auto"}
        }
    ],
    instructions=instructions,
    input="I need to solve the equation 3x + 11 = 14. Can you help me?",
)

print(resp.output)

from openai import OpenAI

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://aihubmix.com/v1"
)

resp = client.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "mcp",
        "server_label": "deepwiki",
        "server_url": "https://mcp.deepwiki.com/mcp",
        "require_approval": "never",
        "allowed_tools": ["ask_question"],
    }],
    input="What transport protocols does the 2025-03-26 version of the MCP spec (modelcontextprotocol/modelcontextprotocol) support?",
)

print(resp.output_text)

注意：

最新的 `codex-mini-latest` 不支持搜索
Computer use 需要配合 Praywright 使用，建议参考官方仓库

已知细节问题：

调用用例复杂
截图大量，耗时久，任务成功率低
或触发 CAPTCHA 验证或 Cloudflare 真人验证，可能遇到无限循环

更新时间：2026-06-01

​使用 (Python 调用)：

使用 (Python 调用)：