---
title: MiniMax-M3
canonical_url: "https://www.modelscope.ai/models/MiniMax/MiniMax-M3"
md_url: "https://www.modelscope.ai/models/MiniMax/MiniMax-M3.md"
repository: MiniMax/MiniMax-M3
last_updated: 2026-07-24
license: other
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - minimax_m3_vl
architectures:
  - MiniMaxM3SparseForConditionalGeneration
parameters: 427.0B
tensor_type:
  - BF16
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 168
stars: 15
tags:
  - multimodal
  - moe
  - agent
  - coding
  - video
---

# MiniMax-M3

> MiniMax-M3 - An open-source model by MiniMax on ModelScope. MiniMax-M3 is a native multimodal model with 1M context. It has 428B parameters and 23B activated parameters.

MiniMax/MiniMax-M3 is a 427.0B-parameter image-text-to-text model on ModelScope. licensed under other.

- **Repository**: MiniMax/MiniMax-M3
- **License**: other
- **Tasks**: image-text-to-text
- **Parameters**: 427.0B
- **Tags**: multimodal, moe, agent, coding, video
- **Downloads**: 168
- **Stars**: 15
- **Last updated**: 2026-07-24

Source: https://www.modelscope.ai/models/MiniMax/MiniMax-M3

---

<div align="center">
  <img width="60%" src="figures/logo.svg" alt="MiniMax">
</div>
<hr>

<p align="center">
  <a href="https://agent.minimax.io/" target="_blank"><img src="https://img.shields.io/badge/MiniMax%20Agent-FF6C37?logo=minimax&logoColor=white" alt="MiniMax Agent"></a>
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  <a href="https://huggingface.co/MiniMaxAI" target="_blank"><img src="https://img.shields.io/badge/Hugging%20Face-FFD21E?logo=huggingface&logoColor=black" alt="Hugging Face"></a>
  <a href="https://github.com/MiniMax-AI/MiniMax-M3" target="_blank"><img src="https://img.shields.io/badge/GitHub-181717?logo=github&logoColor=white" alt="GitHub"></a>
  <a href="https://arxiv.org/abs/2606.13392" target="_blank"><img src="https://img.shields.io/badge/arXiv-2606.13392-B31B1B?logo=arxiv&logoColor=white" alt="arXiv Paper"></a>
  <a href="https://huggingface.co/MiniMaxAI/MiniMax-M3/blob/main/LICENSE" target="_blank"><img src="https://img.shields.io/badge/LICENSE-4CAF50?logo=creativecommons&logoColor=white" alt="LICENSE"></a>
</p>

MiniMax-M3 is a native multimodal model with 1M context. It has ~428B parameters and ~23B activated parameters.

**Highlights:**
- **Native Multimodality:** M3 undergoes mixed-modality training from the very first step, enabling deeper semantic fusion across text, image, and video.
- **Context Scaling via Sparse Attention:** M3 introduces MiniMax Sparse Attention (MSA) to improve long context efficiency. M3 delivers 9× prefill and 15× decode speedups compared to M2 at 1M context, reducing per-token compute to 1/20.
- **Coding & Cowork Capability:** M3 achieves frontier-level performance across long-horizon agentic benchmarks, excelling in both coding and cowork.


<p align="center">
  <img width="100%" src="figures/benchmark.jpeg">
</p>

## MiniMax Sparse Attention (MSA)

M3 is powered by [**MiniMax Sparse Attention (MSA)**](https://github.com/MiniMax-AI/MSA), a high-performance sparse attention operator designed for million-token contexts. Compared with GQA, MSA dramatically reduces the attention compute and memory footprint while preserving model quality.

<p align="center">
  <img width="100%" src="figures/efficiency_gqa_vs_msa.png" alt="GQA vs MSA Efficiency Comparison">
</p>

> 📄 Read the technical report: [arXiv:2606.13392](https://arxiv.org/abs/2606.13392) · [Hugging Face Papers](https://huggingface.co/papers/2606.13392)

## How to Use

- [MiniMax Agent](https://agent.minimax.io/)
- [MiniMax API](https://platform.minimax.io/)

M3 supports three reasoning modes through the `thinking` parameter:
- **`enabled`** — Reasoning is always enabled.
- **`adaptive`** — M3 automatically determines when additional reasoning is beneficial.
- **`disabled`** — Reasoning is disabled to minimize latency and maximize throughput.

## Local Deployment

Download the model:

```bash
hf download MiniMaxAI/MiniMax-M3 --local-dir MiniMax-M3
```

We recommend the following inference frameworks to serve the model:

- [SGLang](https://docs.sglang.io/) - see  [SGLang cookbook](https://docs.sglang.io/cookbook/autoregressive/MiniMax/MiniMax-M3).

- [vLLM](https://github.com/vllm-project/vllm) - see [vLLM recipes](https://recipes.vllm.ai/MiniMaxAI/MiniMax-M3).

- [Transformers](https://github.com/huggingface/transformers) - see [Transformers docs](https://huggingface.co/docs/transformers/model_doc/minimax_m3_vl).

- [KTransformers](https://github.com/kvcache-ai/ktransformers) - see [KTransformers MiniMax-M3 tutorial](https://github.com/kvcache-ai/ktransformers/blob/main/doc/en/kt-kernel/MiniMax-M3-Tutorial.md).

- [unsloth](https://unsloth.ai) - see [tutorial](https://unsloth.ai/docs/models/minimax-m3)

- [ATOM](https://github.com/ROCm/ATOM/tree/main) - see [MiniMax-M3 MXFP4/MXFP8 Usage Guide](https://github.com/ROCm/ATOM/blob/main/recipes/MiniMax-M3.md)

### Inference Parameters

We recommend the following parameters for best performance: `temperature=1.0`, `top_p=0.95`.

## Contact Us

Contact us at [model@minimax.io](mailto:model@minimax.io).