#开源生态

@cohere could you share the full traces? Very important artefact for all to understand this!

MiniMax-M3.1-Flash can now run locally! We made 2-bit GGUF retain 91.5% top 1 accuracy while being 45% smaller. Run on 150GB RAM. MiniMax-M3.1-Flash is the strongest open model for its size & among the best overall. GGUF: https://huggingface.co/unsloth/MiniMax-M3.1-Flash-GGUF Guide: https://unsloth.ai/docs/models/minimax-m3.1-flash

MiniMax M3.1 Flash：太空级性能 + 兔子级速度 🚀🐰 没错，Space Bunny——曾以 200T+ tokens 登顶全球排行榜——正是 M3.1 Flash 的早期版本。 💰重新定义智能体 AI 的性能经济学。M3.1 Flash 在 Cowork（Automation-Bench：62%；ALE：30%）、编程（DeepSWE：74%）和视觉推理（BabyVision：91%）等关键任务上达到前沿级智能水平，成本仅为 M3 的 1/3，速度提升 3 倍。 API 定价（统一费率）：每 1M 输入/输出 tokens 分别为 $0.15 / $0.60，缓存读取为 $0.015 / 1M tokens。 模型权重将于 10 月 23 日发布。 API：http://platform.minimax.io M Plan：https://www.minimax.io/m-plan MiniMax Code 桌面版：https://agent.minimax.io/download

卧槽，MiniMax H3 现在连消费级显卡都能跑了！ FastH3 Omni 正式开源！ 之前只能文生视频，现在直接升级全能模式： 图片、视频、音频都能作为参考 一次生成画面和同步音频 角色、动作、声音都能控制 支持消费级显卡本地部署 这波开源，AI 视频创作的门槛又要降了！🤣

Cindy just crossed 5,000 ⭐ on GitHub! Thank you to everyone building with us. Open-source AI agent that drives your browser, computer and phone. Free 👉 https://github.com/makecindy/cindy

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 GPU servers, TPUs, and Apple Silicon. What matters is what this enables: more affordable experimentation, finer creative control, and more choices about where and how video generation runs. Open models become more useful when the community can improve the methods, adapt the runtime, and build workflows around them. Credit to @haoailab @yisun0618 @lanfeng491270 @guocheng_qian @Alissonerdx @ddalcu @tazmannner @HaochengXiUCB @huggingface @Jhuaplin, and the PTD, DMAD, SparkH3, TorchTPU and FlashML teams - including Zhengming Yu and zechengtang on GitHub. 11 additions to the H3 ecosystem index. Thank you to everyone building, testing, and sharing. https://github.com/MiniMax-AI/awesome-minimax-h3-integration

Open weights. Shared progress. MiniMax H3 is moving fast. We built H3 for video generation with native stereo audio and multimodal reference control. The open-source community is making that capability faster, more accessible, and easier to build on. Recent highlights: • FastH3 — FastVideo, Nuva Lab and NVIDIA: 4-step distillation, now running on DGX Spark and Apple Silicon. • Sol-H3 — NVIDIA’s SANA team: 15 seconds of 768p video + audio in 6.6 seconds on 8×B300, in the team’s warm-inference benchmark.* • VDN — Haocheng Xi and the OpenVDN team: rethinking attention for faster H3 inference, with weights, training and inference code released. • PDD — NVIDIA’s distillation method, brought to H3 by Alibaba PAI as 8-step Acc-LoRAs, now supported in ComfyUI. • LightX2V — 4- and 8-step Turbo LoRAs, with workflows for text, image and reference-conditioned video + audio. Behind every release are people training, optimizing, quantizing, testing and sharing. Special thanks to: @haoailab @nuvalab @NVIDIAAI @xieenze_jr @HaochengXiUCB @ArashVahdat @julberner @LightX2V @ComfyUI And to the individual contributors pushing the work forward: @haozhangml @cxlcl1 @lawrence_cjs @yitongli165665 @haopengl33 @songhan_mit @shanasaimoe Thank you for building with H3 and helping make it faster, more accessible, and more useful for the community. Powerful models go further when we build together. Keep pushing H3. Excited to see what comes next. 🚀 Explore the ecosystem: https://github.com/MiniMax-AI/awesome-minimax-h3-integration

Today @MiniMax_AI released the open weights for MiniMax M3.1 Flash – available now on Tenstorrent Cloud. Explore in the playground or connect to your agent harness. Start building → https://bit.ly/47vU2zc

We have another release you don’t want to miss. @MiniMax_AI M3.1 Flash is now live on Fireworks, day 0 with the open-weights release. Built for agents that run long, multi-step work: → Cowork automation: 62.8 on AutomationBench-AA → Coding: 73.7% on DeepSWE v1.1 → Visual reasoning: 91 on BabyVision → $0.15 input / $0.60 output / $0.015 cached per 1M tokens, half the price of M3 We've heard your feedback that you want more models on serverless. So we're launching more models in preview mode, starting today with M3.1 Flash, available for two weeks. We'll be watching demand, and if the community likes it, we'll keep it around. Try it now → https://bit.ly/4dShASp

We release the most comprehensive 𝗲𝗺𝗽𝗶𝗿𝗶𝗰𝗮𝗹 𝘀𝘁𝘂𝗱𝘆 𝗼𝗳 𝗚𝗣𝗧-𝟲-𝗔𝘀𝘁𝗿𝗮’𝘀 𝗲𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 across 12 simulation benchmarks and 8 real-robot platforms, together with 𝗦𝗲𝗻𝘀𝗲𝗡𝗼𝘃𝗮-𝗥𝗼𝗯𝗼𝗥𝗦𝗜: an open-source harness framework and a 27B model that runs at ~1/50 the cost of GPT-6-Astra. Here's what we found: 🔹 𝗧𝗵𝗲 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗰𝗲𝗶𝗹𝗶𝗻𝗴 𝗶𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝘂𝗰𝗵 𝗵𝗶𝗴𝗵𝗲𝗿. Under a unified protocol (ICL + up to 10× the interaction budget), GPT-6-Astra achieves clear SOTA across all 12 benchmarks: RoboDojo: 27.6% at 1× → 81.9% at 10×, DexJoCo: 87.3% vs. 40.3% prior SOTA, while the LIBERO benchmarks are pushed to saturation. 🔹 𝗧𝗼𝗼 𝗺𝘂𝗰𝗵 𝗯𝘂𝗰𝗸 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗯𝗮𝗻𝗴. Even a simple task like placing pens in a holder costs ~$55 in inference. 🔹 𝗧𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 𝗱𝗼𝗲𝘀𝗻’𝘁 𝘄𝗮𝗶𝘁. Time matters as real robots overheat, and an egg burned while the model was still thinking. Our takeaway: GPT-6-Astra has largely crossed the threshold of 𝗲𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗰𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝗰𝗲, but 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗲𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 is not here yet. 🌐 Project page: https://www.sensenova.ai/has-gpt-6-astra-achieved-embodied-intelligence 📄 Report: https://www.sensenova.ai/gpt-6/assets/docs/has_gpt-6_achieved_embodied_intelligence.pdf 💻 Code & model: https://github.com/OpenSenseNova/SenseNova-RoboRSI

minimax m3.1 flash 现已在 together ai 上线 我们很高兴成为该模型（又名 space bunny）的首发日合作伙伴 m3.1 flash 专为智能体任务、编程、计算机使用 + 网络安全而打造，拥有 1m 上下文窗口并支持视觉 立即试用 https://www.together.ai/models/minimax-m3-1-flash