MiniMax: MiniMax-M3 model card

Captured current version, in full.
direct capture Captured 2026-07-23 04:27:21 UTC Method direct Content hash 5965f13cd579f61b Source https://huggingface.co/MiniMaxAI/MiniMax-M3/raw/main/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
---
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.
## 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.
> 📄 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).