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Minimax M1

MiniMax-M1 is an open-weight reasoning language model with a hybrid Mixture-of-Experts architecture and lightning attention, described as supporting a native context length of one million tokens.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Minimax M1 — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Consider MiniMax-M1 when a task involves long text inputs or extended reasoning, and you can work with model weights or plan a separate serving setup.

Who it is for

  • Developers exploring open-weight reasoning models for language-model applications.
  • Researchers and practitioners working with long documents or other large text inputs.

Documented uses

  • Answer questions over large text inputs.
  • Explore reasoning over long documents.
  • Add reasoning capabilities to a language-model application.
  • Use task-specific prompting for summarization, translation, mathematics or web-development code generation.
Before you choose

Requirements & limits

The model is published in a public GitHub repository under the Apache-2.0 license. The README links to MiniMax-M1-40k and MiniMax-M1-80k downloads on Hugging Face.

  • The README states a one-million-token native context length.
  • The model combines a hybrid Mixture-of-Experts architecture with lightning attention.
  • The README suggests temperature 1.0 and top_p 0.95, and recommends adapting the system prompt to the task.
  • For production serving, the README recommends vLLM; it does not provide complete deployment requirements in the available excerpt.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set MiniMax-M1 inference recommendations

Prepare the README-recommended inference settings and general-purpose system prompt for MiniMax-M1 use.

Before you start

  • Access to a MiniMax-M1 inference setup where temperature, top_p, and a system prompt can be configured.

Steps

  1. Set temperature to 1.0.

  2. Set top_p to 0.95.

  3. For common tasks such as summarization, translation, question answering, or creative writing, set the system prompt to: “You are a helpful assistant.”

Expected outcome

The inference setup uses the README's recommended parameters and general-purpose system prompt.

Guide limitations

  • The source gives recommendations but does not describe a particular inference client, executable command, or validation procedure.
  • The model supports a native context length of 1 million tokens, but no steps for preparing or submitting long inputs are provided.
Documentation 1 ↗
Source-based practical guide

Download MiniMax-M1 model weights

Obtain a listed MiniMax-M1 model variant from Hugging Face as the first stage of deployment.

Before you start

  • Access to the Hugging Face repositories linked in the MiniMax-M1 README.

Steps

  1. Open the Hugging Face repository for MiniMax-M1-40k or MiniMax-M1-80k.

  2. Download the selected model from its repository.

Expected outcome

The selected MiniMax-M1 variant is downloaded and ready for a subsequent serving setup.

Guide limitations

  • The README does not specify download commands or required hardware.
  • The README recommends vLLM for production serving but the available content does not include the serving steps.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
MiniMax-AI/MiniMax-M1 · Official homepage title · retained in its source language
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