# MiniMax-M3.1-Flash

MiniMax open-sourced MiniMax-M3.1-Flash, and vLLM supports it on day one on NVIDIA Blackwell, NVIDIA Rubin and AMD MI350-series GPUs. The model is quantization-aware trained for an NVFP4 KV cache and stores its routed MoE weights in NVFP4, shrinking the checkpoint from 451.5 GB to 250.1 GB and the KV cache from 61,440 to 34,560 bytes per token. On four NVIDIA B300 GPUs with DEP4 deployment and 256K-token sequences, M3.1 fits 76 concurrent sequences where M3 with an FP8 KV cache fits 28.

MiniMax open-sourced MiniMax-M3.1-Flash, and vLLM supports it on day one on NVIDIA Blackwell, NVIDIA Rubin and AMD MI350-series GPUs. The model is quantization-aware trained for an NVFP4 KV cache and stores its routed MoE weights in NVFP4, shrinking the checkpoint from 451.5 GB to 250.1 GB and the KV cache from 61,440 to 34,560 bytes per token. On four NVIDIA B300 GPUs with DEP4 deployment and 256K-token sequences, M3.1 fits 76 concurrent sequences where M3 with an FP8 KV cache fits 28.

MiniMax's open-weight multimodal M3.1 Flash is now free on AI Gateway through Oct 22, and only when MiniMax serves the request. The model targets coding and agentic work with a 1M-token context window for long documents and multi-step agent sessions. Compared with earlier MiniMax models, MiniMax says it improves on complex agentic tasks, coding and cybersecurity, with its best coding results on application development, particularly frontend interactions, targeted bug fixes and component tests.

MiniMax's open-weight multimodal M3.1 Flash is now free on AI Gateway through Oct 22, and only when MiniMax serves the request. The model targets coding and agentic work with a 1M-token context window for long documents and multi-step agent sessions. Compared with earlier MiniMax models, MiniMax says it improves on complex agentic tasks, coding and cybersecurity, with its best coding results on application development, particularly frontend interactions, targeted bug fixes and component tests.