MiniMax Releases Open-Weight M3, a 428B Multimodal Model With 1M-Token Context
MiniMax released open weights for M3, a native multimodal model for coding and agentic workloads that is available on Hugging Face. The company said M3 has about 428 billion total parameters, with roughly 23 billion activated, supports text, image and video inputs, and uses MiniMax Sparse Attention to reach a 1 million-token context window; it also reported benchmark scores of 59.0% on SWE-Bench Pro and 66.0% on Terminal Bench 2.1.
The launch came with broad day-zero inference support. vLLM said M3 runs on Nvidia and AMD hardware, while SemiAnalysis said the model had been added to InferenceX and was already showing optimized performance on Nvidia's B300 Blackwell Ultra, with MI355X benchmarking under way. One day after release, MiniMax highlighted community work on MLX-VLM that improved the decode path for faster decode and a lighter footprint.
From the sources (25 posts)
@minimax_aiRT @RyanLeeMiniMax: Hey everyone — our high-performance MSA kernel library is now open-source. The M3 weights are expected to drop this Fri…
@minimax_aiRT @RyanLeeMiniMax: Hey everyone — our high-performance MSA kernel library is now open-source. The M3 weights are expected to drop this Fri…
@minimax_aiWeights on Friday 🫶
@minimax_aiMiniMax M3, Open-Weight, Now On Hugging Face Weights: MiniMax Sparse Attention:
@minimax_aiRT @lmsysorg: 🎉 SGLang has Day-0 support for MiniMax-M3 from @MiniMax_AI, a native-multimodal MoE reasoning model of ~428B total params (~2…
@minimax_aiRT @RyanLeeMiniMax: On the M3 license — thanks for the feedback on M2.7 You told us prior-approval-for-any-commercial-use was too much. We…
@minimax_aiMiniMax M3, Open-Weight, Now On Hugging Face , with only ~428B parameters and ~23B activated parameters Weights: MiniMax Sparse Attention:
@yacinemtbRT @MiniMax_AI: MiniMax M3, Open-Weight, Now On Hugging Face , with only ~428B parameters and ~23B activated parameters Weights: https://t…
@lmsysorg🎉 SGLang has Day-0 support for MiniMax-M3 from @MiniMax_AI, a native-multimodal MoE reasoning model of ~428B total params (~23B active), 60 layers, 1M context across text, image & video. ✅ Native multimodality: text-image-video fusion from
@adinayakupMiniMax-M3 just dropped on @huggingface ✨ 428B / 23B active ✨ 1M context ✨ MiniMax Sparse Attention (MSA) And it’s not just weights! Day-one full release: - paper - kernel - Transformers support Love how this was released❤️ @MiniMax_AI
@vllm_project🎉 Congrats to @MiniMax_AI on releasing MiniMax M3! Frontier coding and agentic capabilities, native image and video input, computer use, and a 1M-token context window, all in a single open model. At the heart of M3 is MSA, a new sparse att
@vllm_projectDay-0 goes beyond inference: NeMo RL from @NVIDIAAI also supports MiniMax M3 on day 0, with vLLM powering rollout generation. 💡 A reference GRPO recipe is ready, so you can start post-training M3 for your own agentic workflows right away.
@nvidiaaiCongrats to the @MiniMax_AI team on the release of MiniMax M3, a long-context multimodal model for text, image, and video reasoning. 🙌 Try it today with our free GPU-accelerated endpoint on Details:
@zephyr_z9with only ~428B parameters and ~23B activated parameters Smaller than I expected
@minimax_aiM3 open weight just dropped and it's live on @Modular cloud on day zero with up to a 1M-context and MSA architecture kernel-to-cloud optimization is exactly what M3 needs glad to have @Modular with us from the start
@_akhaliqRT @novita_labs: 🤗 MiniMax M3 from @MiniMax_AI is now live on @huggingface — supported by Novita. Open weights. ~428B total parameters. ~2…
@minimax_aiM3 is now live on @parasail_io 🚀
@clattner_llvmM3 from @MiniMax_AI is now live on Modular Cloud, day zero. Open weights, 1M-token context, multimodal. MSA is a new attn architecture and getting its performance benefits requires whole-stack optimization. That's what we built Modular fo
@unslothaiMiniMax M3 can now be run locally!🔥 MiniMax-M3 is a new 428B (23B active) open model with 1M context that performs on par with Gemini 3.1 Pro. Run Dynamic 2-bit GGUF on 138GB RAM/VRAM or 3-bit on 165GB. GGUF: Guid
@minimax_aiRun M3 locally today with @UnslothAI
@far__elRT @MiniMax_AI: Introducing MiniMax M3: The First Open-Weights Model to Combine Three Frontier Capabilities - Coding & Agentic Frontier: 5…
@far__elRT @MiniMax_AI: M3 would never 🙂↔️ As a matter of fact, the weights are now open, too.
@danielhanchenRT @UnslothAI: MiniMax M3 can now be run locally!🔥 MiniMax-M3 is a new 428B (23B active) open model with 1M context that performs on par w…
@togethercomputeMiniMax-M3 from @MiniMax_AI is now available on Together AI. It’s an open-weight native multimodal model with 1M context, MiniMax Sparse Attention, and thinking / non-thinking modes. Together AI is MiniMax’s preferred cloud partner, with
@minimax_aithe kernels are doing the lord's work today, day-0 on @vllm_project, verified on nvidia and amd. go read the writeup 👇