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 |
How to Use
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:
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 to serve MiniMax-M3. Please refer to our SGLang Deployment Guide.
vLLM
We recommend using vLLM to serve MiniMax-M3. Please refer to our vLLM Deployment Guide.
Transformers
We recommend using Transformers to serve MiniMax-M3. Please refer to our Transformers Deployment Guide.
ModelScope
You can also get model weights from ModelScope.
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.
Contact Us
Contact us at model@minimax.io.