README.md

---
pipeline_tag: image-text-to-text
license: other
license_name: minimax-community
license_link: LICENSE
library_name: transformers
tags:
- multimodal
- moe
- agent
- coding
- video
base_model:
- MiniMaxAI/MiniMax-M3
---

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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.

## Model Details
| | |
| --- | --- |
| Architecture | MoE + MSA (MiniMax Sparse Attention) |
| Total Parameters | ~428B |
| Activated Parameters | ~23B |
| Experts | 128 (4 active per token) |
| Layers | 60 |
| Context Length | 1M tokens |
| Modalities | Text, Image, Video |
| Precision | bfloat16 |
| Transformers | ≥ 4.52.4 (`trust_remote_code=True`) |
| License | [MiniMax Community License](LICENSE) |
<p align="center">
  <img width="100%" src="figures/benchmark.jpeg">
</p>

## How to Use

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

M3 supports two reasoning modes:
- **thinking** — for complex reasoning, agentic tasks, and long-horizon collaboration.
- **non-thinking** — for latency-sensitive scenarios such as chat and code completion.

## Local Deployment

Download the model:

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

We recommend the following inference frameworks (listed alphabetically) to serve the model:

### SGLang

We recommend using [SGLang](https://docs.sglang.io/) to serve MiniMax-M3. Please refer to our [SGLang Deployment Guide](./docs/sglang_deploy_guide.md).

### vLLM

We recommend using [vLLM](https://github.com/vllm-project/vllm) to serve MiniMax-M3. Please refer to our [vLLM Deployment Guide](./docs/vllm_deploy_guide.md).

### Transformers

We recommend using [Transformers](https://github.com/huggingface/transformers) to serve MiniMax-M3. Please refer to our [Transformers Deployment Guide](./docs/transformers_deploy_guide.md).

### ModelScope

You can also get model weights from [ModelScope](https://modelscope.cn/models/MiniMax/MiniMax-M3).

### Inference Parameters

We recommend the following parameters for best performance: `temperature=1.0`, `top_p=0.95`, `top_k=40`. Default system prompt:

```
You are a helpful assistant. Your name is MiniMax-M3 and was built by MiniMax.
```

## Tool Calling Guide

Please refer to our [Tool Calling Guide](./docs/tool_calling_guide.md).

## Contact Us

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