MiniMax M3.1 Flash open weights: 428B total/23B active, 1M context, ~250GB quantized
Breakdown of the released weights: 428B total and 23B active params, 1M context, text/image/video input, sparse attention in every layer, ~250GB thanks to train-time quantization, a bundled speculative draft head, day-0 vLLM/SGLang support, and 62.8 vs 60.5 over DeepSeek V4.1 Flash on Automation-Bench; the license is free with credit but needs permission above $20M revenue.
MiniMax M3.1 Flash weights are out. 428B total, 23B active, 1M context, and it takes text, image and video as input. And it's cheap for inference. Sparse attention in every layer. The experts and attention are trained with quantization from the start, so the shipped weights are about 250GB instead of the 800+ a 428B model would take in BF16. Plus, there's a speculative decoding draft head in the repo. vLLM and SGLang support on day 0. In their own table it beats DeepSeek V4.1 Flash 1017 on Automation-Bench, 62.8 to 60.5, and GPT-5.6 Sol and Opus 5 on the same bench by more than 10 points. The license is not MIT. Free for commercial use with a Built with MiniMax credit, and you need their permission above $20M in yearly revenue. For me and for almost every startup that changes nothing. 250GB does not fit my 96GB of VRAM, so I need a smaller quant to run it here. Give the community a few days, it always comes. DeepSeek’s engine on Tuesday, MiniMax weights on Friday. Chinese labs are shipping open weights every few days now. @MiniMax_AI
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