Model Library/MiniMax M1
MiniMax M1

MiniMax M1

minimaxai/minimax-m1-80k
by MiniMax
Input price
$0.55/ M tokens
Output price
$2.2/ M tokens
Context window
977Ktokens
Max output
39Ktokens

MiniMax-M1: The World's First Open-Weight, Large-Scale Hybrid Attention Inference Model MiniMax-M1 adopts a Mixture of Experts (MoE) architecture and integrates the Flash Attention mechanism. The model contains a total of 456 billion parameters, with 45.9 billion parameters activated per token. Natively, the M1 model supports a context length of 1 million tokens—8 times that of DeepSeek R1. Additionally, by combining the CISPO algorithm with an efficient hybrid attention design for reinforcement learning training, MiniMax-M1 achieves industry-leading performance in long-context reasoning and real-world software engineering scenarios.

Características

API serverless

Documentación

minimaxai/minimax-m1-80k is available via Novita's serverless API, where you pay per token. There are several ways to call the API, including OpenAI-compatible endpoints with exceptional reasoning performance.

Serverless disponible

Ejecuta consultas de inmediato, paga solo por el uso

Entrada$0.55 / M Tokens
Salida$2.2 / M Tokens
  1. Obtén tu clave de API

    Regístrate y crea una clave para ejecutar el código de abajo. La API es compatible con OpenAI.

  2. Make your first request

    The API is OpenAI-compatible: point any OpenAI SDK at the base URL and pass this model ID.

    Base URL
    https://api.novita.ai/openai
    Model ID
    minimaxai/minimax-m1-80k
    API Endpoint
    chat/completions
    1from openai import OpenAI
    2
    3client = OpenAI(
    4    api_key="<Your API Key>",
    5    base_url="https://api.novita.ai/openai"
    6)
    7
    8response = client.chat.completions.create(
    9    model="minimaxai/minimax-m1-80k",
    10    messages=[
    11        {"role": "system", "content": "You are a helpful assistant."},
    12        {"role": "user", "content": "Hello, how are you?"}
    13    ],
    14    max_tokens=40000,
    15    temperature=0.7
    16)
    17
    18print(response.choices[0].message.content)