MiniMax-M3
MiniMax-M3 es un modelo multimodal de mezcla de expertos de 427B parámetros de MiniMax que admite entradas de imagen, vídeo y texto con capacidades de codificación y agentes.
Design Arena
Design Arena clasifica modelos en tareas reales de front-end y diseño —sitios web, componentes de UI, visualización de datos, SVG y más— mediante votos humanos cara a cara, puntuados como rating ELO.| Categoría | ELO | Tasa de victoria | Puesto |
|---|---|---|---|
| Code | 1290 | 55.0% | #16 |
| Website | 1289 | 55.1% | #17 |
| 3D | 1287 | 55.4% | #22 |
| UI component | 1284 | 53.3% | #20 |
| Game dev | 1280 | 51.5% | #23 |
Precios
por 1M tokensDescripción del Modelo
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), 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 · Hugging Face Papers
How to Use
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:
hf download MiniMaxAI/MiniMax-M3 --local-dir MiniMax-M3
We recommend the following inference frameworks (listed alphabetically) to serve the model:
SGLang - see SGLang cookbook.
vLLM - see vLLM recipes.
Transformers - see Transformers docs.
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.
Regístrate para leer casos de estudio completos, acceder a métricas detalladas y recibir todos los reportes.
Regístrate para leer casos de estudio completos, acceder a métricas detalladas y recibir todos los reportes.