MiniMax-M1-80k 现已在 SiliconFlow 上可用

2025年6月17日

混合注意力+MoE架构 M1结合了专家混合路由的效率与闪电注意力的深度，使其在扩展的同时保持长序列推理质量。
为代理和工具优化 M1支持扩展的上下文和强大的推理，非常适合于自主代理、文档分析和沙盒软件开发等应用。
数学、编码和推理 基准测试显示，M1在需要符号推理、结构化Output和复杂指令执行的任务中与顶级模型竞争。

快速开始

在SiliconFlow模型广场上尝试MiniMax-M1-80k模型。

快速访问API

以下Python示例演示了如何通过SiliconFlow的API端点调用MiniMax-M1-80k模型。提供给开发者的更详细的API规范。

from openai import OpenAI

url = 'https://api.siliconflow.com/v1/'
api_key = 'your api_key'

client = OpenAI(
    base_url=url,
    api_key=api_key
)

# Send a request with streaming output
content = ""
reasoning_content = ""
messages = [
    {"role": "user", "content": "Who are the legendary athletes of the Olympics?"}
]
response = client.chat.completions.create(
    model="MiniMaxAI/MiniMax-M1-80k",
    messages=messages,
    stream=True,  # Enable streaming output
    max_tokens=4096,
    extra_body={
        "thinking_budget": 1024
    }
)
# Gradually receive and process the response
for chunk in response:
    if chunk.choices[0].delta.content:
        content += chunk.choices[0].delta.content
    if chunk.choices[0].delta.reasoning_content:
        reasoning_content += chunk.choices[0].delta.reasoning_content

# Round 2
messages.append({"role": "assistant", "content": content})
messages.append({'role': 'user', 'content': "Continue"})
response = client.chat.completions.create(
    model="MiniMaxAI/MiniMax-M1-80k",
    messages=messages,
    stream=True
)

from openai import OpenAI

url = 'https://api.siliconflow.com/v1/'
api_key = 'your api_key'

client = OpenAI(
    base_url=url,
    api_key=api_key
)

# Send a request with streaming output
content = ""
reasoning_content = ""
messages = [
    {"role": "user", "content": "Who are the legendary athletes of the Olympics?"}
]
response = client.chat.completions.create(
    model="MiniMaxAI/MiniMax-M1-80k",
    messages=messages,
    stream=True,  # Enable streaming output
    max_tokens=4096,
    extra_body={
        "thinking_budget": 1024
    }
)
# Gradually receive and process the response
for chunk in response:
    if chunk.choices[0].delta.content:
        content += chunk.choices[0].delta.content
    if chunk.choices[0].delta.reasoning_content:
        reasoning_content += chunk.choices[0].delta.reasoning_content

# Round 2
messages.append({"role": "assistant", "content": content})
messages.append({'role': 'user', 'content': "Continue"})
response = client.chat.completions.create(
    model="MiniMaxAI/MiniMax-M1-80k",
    messages=messages,
    stream=True
)

from openai import OpenAI

url = 'https://api.siliconflow.com/v1/'
api_key = 'your api_key'

client = OpenAI(
    base_url=url,
    api_key=api_key
)

# Send a request with streaming output
content = ""
reasoning_content = ""
messages = [
    {"role": "user", "content": "Who are the legendary athletes of the Olympics?"}
]
response = client.chat.completions.create(
    model="MiniMaxAI/MiniMax-M1-80k",
    messages=messages,
    stream=True,  # Enable streaming output
    max_tokens=4096,
    extra_body={
        "thinking_budget": 1024
    }
)
# Gradually receive and process the response
for chunk in response:
    if chunk.choices[0].delta.content:
        content += chunk.choices[0].delta.content
    if chunk.choices[0].delta.reasoning_content:
        reasoning_content += chunk.choices[0].delta.reasoning_content

# Round 2
messages.append({"role": "assistant", "content": content})
messages.append({'role': 'user', 'content': "Continue"})
response = client.chat.completions.create(
    model="MiniMaxAI/MiniMax-M1-80k",
    messages=messages,
    stream=True
)