> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aihubmix.com/llms.txt
> Use this file to discover all available pages before exploring further.

# 阿里通义系列

## Qwen 3 系列

Qwen3 系列是阿里推出的新一代开源大模型，能力大幅跃升：在代码理解、数学推理、多语言表达、复杂推断任务上，比肩甚至超越了目前市面上的顶级模型（如 o1、DeepSeek-R1）。**它的核心突破在于引入了「思考模式」与「非思考模式」切换机制，让模型在面对不同难度任务时，自主调节推理深度，实现了速度与精度的双优平衡。** 旗舰版 Qwen3-235B 采用稀疏激活，仅用 22B 参数推理，兼顾成本和卓越能力。全系模型全面开源，涵盖从轻量到超大规模需求。

**1. 基础用法：** 用 OpenAI 兼容格式转发。\
**2. 工具调用：** 常规 Tools 调用支持 OpenAI 兼容格式（适用于 V2.5、V3），而 MCP Tools 依赖 `qwen-agent`，需要先运行指令安装依赖：`pip install -U qwen-agent mcp`。
更多细节可以参考[阿里官方文档](https://huggingface.co/Qwen/Qwen3-235B-A22B)

<CodeGroup>
  ```py 基础用法 theme={null}
  from openai import OpenAI

  client = OpenAI(
      api_key="sk-***", # 🔑 换成你在 AiHubMix 生成的密钥
      base_url="https://aihubmix.com/v1",
  )

  completion = client.chat.completions.create(
      model="Qwen/Qwen3-30B-A3B",
      messages=[
          {
              "role": "user",
              "content": "Explain the Occam's Razor concept and provide everyday examples of it"
          }
      ],
      stream=True
  )

  # 某些 chunk 对象可能没有 choices 属性或 choices 是一个空列表，处理方法：
  for chunk in completion:
      if hasattr(chunk.choices, '__len__') and len(chunk.choices) > 0:
          if hasattr(chunk.choices[0].delta, 'content') and chunk.choices[0].delta.content is not None:
              print(chunk.choices[0].delta.content, end="")
  ```

  ```py Tools theme={null}
  from openai import OpenAI

  client = OpenAI(
      api_key="sk-***", # 🔑 换成你在 AiHubMix 生成的密钥
      base_url="https://aihubmix.com/v1",
  )

  # 定义工具
  tools = [
      {
          "type": "function",
          "function": {
              "name": "get_current_weather",
              "description": "获取指定位置的当前天气",
              "parameters": {
                  "type": "object",
                  "properties": {
                      "location": {
                          "type": "string",
                          "description": "城市名称，如北京、上海等"
                      },
                      "unit": {
                          "type": "string",
                          "enum": ["celsius", "fahrenheit"],
                          "description": "温度单位"
                      }
                  },
                  "required": ["location"]
              }
          }
      }
  ]

  # 创建聊天完成请求，包含工具定义
  completion = client.chat.completions.create(
      model="Qwen/Qwen3-30B-A3B", #2.5 和 3 都支持，QwQ 不支持
      messages=[
          {
              "role": "user",
              "content": "北京今天的天气怎么样？"
          }
      ],
      tools=tools,
      tool_choice="auto",  # 让模型自行决定是否使用工具
      stream=True
  )

  # 用于收集工具调用信息的字典
  tool_calls = {}

  # 处理流式响应
  for chunk in completion:
      if not hasattr(chunk.choices, '__len__') or len(chunk.choices) == 0:
          continue
          
      delta = chunk.choices[0].delta
      
      # 处理文本内容
      if hasattr(delta, 'content') and delta.content:
          print(delta.content, end="")
      
      # 处理工具调用
      if hasattr(delta, 'tool_calls') and delta.tool_calls:
          for tool_call in delta.tool_calls:
              if not hasattr(tool_call, 'index'):
                  continue
                  
              idx = tool_call.index
              if idx not in tool_calls:
                  tool_calls[idx] = {"name": "", "arguments": ""}
                  
              if hasattr(tool_call, 'function'):
                  if hasattr(tool_call.function, 'name') and tool_call.function.name:
                      tool_calls[idx]["name"] = tool_call.function.name
                  if hasattr(tool_call.function, 'arguments') and tool_call.function.arguments:
                      tool_calls[idx]["arguments"] += tool_call.function.arguments

  # 完成后，打印收集到的工具调用信息
  for idx, info in tool_calls.items():
      if info["name"]:
          print(f"\n工具调用：{info['name']}")
      if info["arguments"]:
          print(f"参数：{info['arguments']}")

  ```

  ```py MCP Tools theme={null}
  from qwen_agent.agents import Assistant
  import os

  # Define LLM
  llm_cfg = {
      'model': 'Qwen/Qwen3-30B-A3B',

      # Use a custom endpoint compatible with OpenAI API:
      'model_server': 'https://aihubmix.com/v1',
      'api_key': os.getenv('AIHUBMIX_API_KEY'),

      # Other parameters:
      # 'generate_cfg': {
      #         # Add: When the response content is `<think>this is the thought</think>this is the answer;
      #         # Do not add: When the response has been separated by reasoning_content and content.
      #         'thought_in_content': True,
      #     },
  }

  # Define Tools
  tools = [
      {'mcpServers': {  # You can specify the MCP configuration file
              'time': {
                  'command': 'uvx',
                  'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
              },
              "fetch": {
                  "command": "uvx",
                  "args": ["mcp-server-fetch"]
              }
          }
      },
    'code_interpreter',  # Built-in tools
  ]

  # Define Agent
  bot = Assistant(llm=llm_cfg, function_list=tools)

  # Streaming generation
  messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
  for responses in bot.run(messages=messages):
      pass
  print(responses)
  ```
</CodeGroup>

## QvQ、Qwen 2.5 和 QwQ 系列

用 OpenAI 的兼容格式转发即可，区别在于流式调用的提取，需要剔除为空的 `chunk.choices[0].delta.content`，参考如下。

**1. QvQ、Qwen 2.5 VL：** 图片识别\
**2. QwQ：** 文本任务

<Info>
  `Qwen/QVQ-72B-Preview` 是基于 `Qwen2-VL-72B` 构建的开源多模态推理模型，专注于视觉推理和跨模态任务。
</Info>

<CodeGroup>
  ```py Qwen 2.5 VL theme={null}
  from openai import OpenAI
  import base64
  import os

  client = OpenAI(
      api_key="sk-***", # 🔑 换成你在 AiHubMix 生成的密钥
      base_url="https://aihubmix.com/v1",
  )

  image_path = "yourpath/file.png"

  # 读取并编码图片
  def encode_image(image_path):
      if not os.path.exists(image_path):
          raise FileNotFoundError(f"图片文件不存在：{image_path}")
      
      with open(image_path, "rb") as image_file:
          return base64.b64encode(image_file.read()).decode('utf-8')

  # 获取图片的 base64 编码
  base64_image = encode_image(image_path)

  # 创建包含文本和图像的消息
  completion = client.chat.completions.create(
      model="qwen2.5-vl-72b-instruct", #qwen2.5-vl-72b-instruct 或 Qwen/QVQ-72B-Preview
      messages=[
          {
              "role": "user",
              "content": [
                  {"type": "text", "text": "请详细描述这张图片，包括图片中的内容、风格和可能的含义。"},
                  {
                      "type": "image_url",
                      "image_url": {
                          "url": f"data:image/png;base64,{base64_image}"
                      }
                  }
              ]
          }
      ],
      stream=True
  )

  for chunk in completion:
      # 安全地检查是否有内容
      if hasattr(chunk.choices, '__len__') and len(chunk.choices) > 0:
          if hasattr(chunk.choices[0].delta, 'content') and chunk.choices[0].delta.content is not None:
              print(chunk.choices[0].delta.content, end="")
  ```

  ```py QwQ theme={null}
  from openai import OpenAI

  client = OpenAI(
      api_key="sk-***", # 🔑 换成你在 AiHubMix 生成的密钥
      base_url="https://aihubmix.com/v1",
  )

  completion = client.chat.completions.create(
      model="Qwen/QwQ-32B",
      messages=[
          {
              "role": "user",
              "content": [
                  {"type": "text", "text": "支配宇宙的元规则是什么？"}
              ]
          }
      ],
      stream=True
  )

  for chunk in completion:
      if hasattr(chunk.choices, '__len__') and len(chunk.choices) > 0:
          if hasattr(chunk.choices[0].delta, 'content') and chunk.choices[0].delta.content is not None:
              print(chunk.choices[0].delta.content, end="")
  ```
</CodeGroup>

***

更新时间：2026-06-01