MiniMax’s model card lists MiniMax-M3-MXFP8 as an MXFP8-quantized variant of MiniMax-M3, tagged for image-text-to-text tasks. It describes M3 as a native multimodal model with a 1M-token context, about 428B total parameters and about 23B activated parameters, trained from the first step across text, image and video. The model uses MiniMax Sparse Attention, which MiniMax says is designed for million-token contexts and reduces attention compute and memory use compared with GQA. MiniMax also reports 9× prefill and 15× decode speedups over M2 at a 1M-token context, with per-token compute reduced to one-twentieth; these figures are the company’s claims.