MiniMax × @Pickford_AI bring Whispers to the Sydney Opera House during #NeurIPS2026.
Cinema that rewrites and reanimates itself in response to its audience.
December 7 · Private screening
MiniMax M3.1 Flash meets the @UnslothAI community. 🐙🦥✨
Unsloth’s Dynamic GGUFs bring our 428B MoE model to a 143.9GB 2-bit footprint, with 91.5% top-1 token agreement in their evaluation across seven domains.
That means a lower memory barrier to self-hosting, deployment
More reference control. Fewer denoising steps. More places to run H3.
The latest community contributions span 8-step reference-conditioned video + audio, 4-step distillation, sparse attention, and reference-token compression - alongside serving and workflow integrations across
MiniMax M3.1 Flash is now on @tenstorrent Cloud -bringing frontier AI to Tensix silicon, Ethernet scale-out, and an open-source stack from the team led by Jim Keller.
Open weights. Diverse architectures. An open, heterogeneous AI future.🫡
Thanks @FireworksAI_HQ for bringing M3.1 Flash to serverless on day 0! Looking sharp with the rebrand! 🎆
Try M3.1 Flash in serverless preview for your next multi-step agent workflow 🐰
M3.1 Flash weights are now open 🔓
Thanks to @togethercompute for being a day-0 launch partner and bringing M3.1 Flash to production on the AI Native Cloud. Open weights, no GPUs to manage. Great building together! 🐰
We’re partnering with Inferact to advance production inference for MiniMax models, with engineering improvements contributed back to @vllm_project.
Thanks to the Inferact team for supporting M3.1 Flash in production from day one. Looking forward to building together!
From inference to RL, ready from day one. 🐰
Thanks to the teams behind SGLang, and Miles @sgl_project alongside @NVIDIAAI and @AMD, for supporting MiniMax M3.1 Flash. Great building together!
MiniMax M3.1 Flash weights are now public. 428B parameters total, 23B activated per token.
Agentic performance: A generational leap in agentic tasks, coding, and cybersecurity — 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam, and
