#模型发布

Qwen3.8 Max by @Alibaba_Qwen is live on OpenRouter. The new flagship has 2.4T parameters (95B active) and is built for long-horizon coding, research, and multimodal agent work. Open weights are due next week, a first for a Qwen Max-class model. https://openrouter.ai/qwen/qwen3.8-max

Big news: MiniMax-H3 by @MiniMax_AI is now the #1 open model in Video Arena: across both Text-to-Video and Image-to-Video. This is +280pts over the next best open model, hunyuan-video-1.5, and a huge improvement from Hailuo-2.3 at #27 (1199 pts) and Hailuo-02-pro at #28 (1197 pts). In Image-to-Video specifically, it scored 1476 pts, just 2 pts away from Dreamina Seedance-2.0 (1478), making it tied for#1 overall! Learn more about how MiniMax-H3 performed in Text-to-Video Arena in thread.

An internal version of our next major model produced 10 new results on long-standing open problems in mathematics and theoretical computer science, using roughly $2,000 worth of tokens at GPT-5.6 Sol API rates.

Today, we’re releasing Athena (mvrko-sim-1), the flagship Large Event Model from @markopolo_ai that beats GPT-5.6, Claude Opus 4.8, and Claude Sonnet 5 in OPeRa public benchmark for Shopping behaviour prediction! Athena introduces a completely different approach to understanding digital behavior. Most systems record actions after they happen. Athena models the sequence behind them to predict what a shopper will do next and exactly where they will do it, before the action even takes place. Athena vs. frontier models: We evaluated Athena on the full OPeRA test set alongside GPT-5.6, GPT-4.1, Claude Sonnet 5, and Claude Opus 4.8. Athena achieved 24.5% strict exact match scoring the highest among the five models evaluated. Athena is not a general-purpose language model. It is a Large Event Model built to understand behavior as a connected sequence. It combines the page someone is viewing, the actions that brought them there, and the goal they are trying to complete and then predicts the exact next browser action and target element. That distinction matters. Today’s digital systems largely react after someone clicks, abandons, purchases, or leaves. Athena creates the foundation for systems that can understand what is likely to happen before the outcome. Together with @RubaiyatFarhan, and the team at @markopolo_ai, we built Athena from years of work on shopper behavior, digital journeys, and behavioral intelligence. Commerce was our first proving ground. Great thing is, we’re now releasing Athena publicly so builders can adapt the model to new datasets, industries, and problems. We’re seeing early usecases in Cyber Security, Gaming, Mobile App Ecosystem, Retail and more! Athena is now live on Hugging Face! We built the foundation for relevance and predictability! Now, let’s see what the world builds on top of it. Links in the first reply.

Qwen 3.8 Max is now live in Command Code Go. … and it's going open weight!! most importantly, next week open-weights of Qwen3.8-Max and Qwen3.8-27B will be released! 🔹 2.4T params MoE flagship 🔹 Input: $2 Output: $6 🔹 Cache: $0.25 / M tokens 3.8 is 20% cheaper than 3.7

MiniMax H3 is native in ComfyUI, day zero with the weights. Model highlights: → Text-to-video, prompt only → Image-to-video → First-and-last-frame - control the opening frame, the closing frame, or both → Reference-to-video - carry a subject, a motion, or a voice through the clip → In-place editing - modify a shot you already have instead of regenerating it The capability @MiniMax_AI leads with is multimodal context understanding, and it's what collapses those five tasks into one model. H3 takes images, audio, and video together and resolves them against a prompt that explains how they relate. Download the workflows & learn more in our blog 👇

MiniMax H3 open weights are live on fal, day 0.

I’m seeing way better videos coming from H3 than 2.5 on the timeline. And more of them. I think cost is a big part of it, plus the speed of inference, and custom audio uploads working as intended. My thesis holds still that at a certain point unless you are actually fixing the pressure points creatives have with your tools, cost and speed are going to win. Unlimited generations are NOT a pipe team. It all comes down to hourly costs divided by users. At some point if you can scale you’ll run at a surplus and can run unlimited.

DeepSeek V4-Flash isn’t just cheaper per token. It reportedly completes the same benchmark tasks as Fable 5 at 105× lower total cost, according to @ArtificialAnlys ! That's precisely why the Flash release is, for me, the DeepSeek 2.0 moment. It will cause a huge stir.

是的，非索菲群确实存在：这一陈述是 Astra——我们的下一个重大模型——所证明的众多新的漂亮结果之一。 我们正在发布 10 个这样的 Astra 证明，每个都附有完整的 Lean 证书和思维链（CoT）详解。这些结果范围广泛，从冯·诺依曼代数（对 Connes 刚性猜想的反证）到高维球填充、电路复杂度、多色图中的单色三角形等问题的更优界，等等。 更多想法见：https://openai.com/index/ten-advances-in-mathematics/