Home › Language Models › MiniMax-M1

MiniMax-M1 - AI Language Models Tool

Overview

MiniMax-M1 is an open-weight, large-scale reasoning model that combines a hybrid Mixture-of-Experts (MoE) architecture with a custom "lightning" attention mechanism to deliver ultra-long context and efficient test-time compute. The project publishes two released variants (MiniMax-M1-40k and MiniMax-M1-80k) and reports a native context window of up to 1,000,000 tokens and a model family built on a 456B-parameter foundation with ~45.9B parameters activated per token. According to the authors, the lightning attention design reduces FLOP costs substantially at long generation lengths, and the team trained the model with a reinforcement-learning pipeline that introduces CISPO (a clipped importance-sampling variant) to improve RL stability and scaling. ([github.com](https://github.com/MiniMax-AI/MiniMax-M1)) The release is positioned for reasoning-heavy and software-engineering tasks: benchmark tables in the project materials highlight strengths on long-context benchmarks, agentic tool use (TAU-bench), and software-engineering suites (SWE-bench). The weights and model cards are published on Hugging Face for both 40K and 80K "thinking-budget" variants, and the authors recommend vLLM for production serving due to its memory and batching efficiencies. Media coverage and community discussion emphasize the model's long-context capabilities and the novel RL scaling claims, while some users report usability and resource challenges when running very large workloads locally. ([huggingface.co](https://huggingface.co/MiniMaxAI/MiniMax-M1-80k?utm_source=openai))

GitHub Statistics

  • Stars: 3,031
  • Forks: 266
  • Contributors: 4
  • License: Apache-2.0
  • Primary Language: Python
  • Last Updated: 2025-07-07T11:57:03Z

The GitHub repository is active and documented: it is published under an Apache-2.0 license and shows strong early adoption for an open-weight LLM (≈3k stars, ≈266 forks, 4 listed contributors). The README includes a technical report, model files (safetensors/index), example configs, and deployment guidance referencing vLLM and Transformers. Issue activity is present but light; the repo lists open issues and a small set of contributors, suggesting a project maintained by a compact core team rather than a broad contributor base. Overall community health is solid for a specialized, large-model release but users should expect to rely on the provided docs and Hugging Face assets for most onboarding. ([github.com](https://github.com/MiniMax-AI/MiniMax-M1))

Installation

Install via pip:

git clone https://github.com/MiniMax-AI/MiniMax-M1.git
python -m pip install --upgrade pip
pip install vllm transformers safetensors huggingface-hub accelerate
python -c "from huggingface_hub import snapshot_download; snapshot_download('MiniMaxAI/MiniMax-M1-80k')"
python -c "from huggingface_hub import snapshot_download; snapshot_download('MiniMaxAI/MiniMax-M1-40k')"

Key Features

  • Native 1,000,000-token context window for ultra-long documents and chains of thought.
  • Hybrid Mixture-of-Experts backbone activating ~45.9B params per token for efficiency.
  • Lightning attention mechanism reduces FLOPs for very long generations (authors report ~25% vs DeepSeek R1 at 100k tokens).
  • CISPO: RL algorithm that clips importance-sampling weights to stabilize large-scale policy updates.
  • Two inference variants (40K and 80K thinking budgets) tuned for different extended-generation needs.
  • Function-calling support and recommendations for vLLM-based deployment for production serving.
  • Open-source release under Apache-2.0 with weights available on Hugging Face for community use.

Community

MiniMax-M1 has attracted strong attention since release — the GitHub repo shows ~3k stars and ~266 forks, and model pages on Hugging Face host downloads and community Spaces. Coverage from outlets (VentureBeat, InfoQ) highlights the model's 1M-token context and RL-efficiency claims; community threads (Reddit, GitHub issues) praise long-context performance but note practical deployment and resource challenges for large local runs. The maintainers provide docs, a technical report, and Hugging Face model cards to help adoption. ([github.com](https://github.com/MiniMax-AI/MiniMax-M1))

Last Refreshed: 2026-01-09

Key Information

  • Category: Language Models
  • Type: AI Language Models Tool
Visit Official Website

Related Tools

Qwen2.5-7B

Qwen2.5-7B is a large language model designed for text generation, featuring improvements in coding, mathematics, instruction following, long text generation, and multilingual support. It supports context lengths up to 128K tokens and is intended for sophisticated NLP tasks.
DeepSeek-V3

Open‑weight flagship LLM (base and chat) with strong reasoning and tool‑use; runs with SGLang/LMDeploy/vLLM and permissive model license.
Llama 3

Llama 3 is an open access large language model (LLM) released by Meta, available in various configurations (8B and 70B parameters) with capabilities for fine-tuning and integrations into platforms like Hugging Face, Google Cloud, and Amazon SageMaker.
UNfilteredAI-1B

A large-scale text generation model designed for creative and unconstrained content production without traditional filtering.
Shuttle-3

Shuttle-3 is a state-of-the-art language model designed for high-quality text generation, particularly suited for complex chat, multilingual communication, and reasoning tasks. It is fine-tuned from the Qwen-2.5-72b-Instruct model and designed to emulate high-quality prose similar to Claude 3 models.
WizardLM

WizardLM is a state-of-the-art large language model designed for complex chat, multilingual tasks, reasoning, and agent functionalities. It features an AI-powered pipeline (Auto Evol-Instruct) that optimizes instruction datasets for improved performance across various domains and leverages Arena Learning for an expanded learning pool of challenging instruction data.