#模型发布

推出 Gemini 4 Argon——我们的全新前沿模型。 它专为编码、企业知识工作和网络安全防御等复杂工作流而构建——今天起通过我们的 Fairwind Program 向一组受信任的测试者推出。

Half a million downloads in a month. Today, our open source family takes another step forward. Thank you for the incredible support behind our first-generation models. We’re excited to introduce TwIL-LM3-Pro. At just 3.6 billion parameters, it brings powerful reasoning to everyday computers, with quantized builds that run locally. No cloud required. In our evaluation: Formal logic: Highest recorded headline score among the small models compared—beating China’s VibeThinker-3B by 35% and Qwen3.5-4B by 24%, and Liquid AI’s LFM2.5-8B-A1B by 47%. Broader reasoning: 95% on SVAMP and 64.1% on MuSR, the highest recorded scores among the small models compared. BIG-Bench Hard’s logic subset: 95.4%, compared with VibeThinker-3B’s 61.1%. We believe AI is entering a post-training era. The advantage will increasingly belong to companies with the best pipelines and those that can produce capable, personalized intelligence faster and more efficiently, then put it on devices people already own. That’s what we’re building at webAI. And we’re only beginning to share what’s coming out of our lab. Coming soon: Meridian, our family of frontier-class models built to run on device. Our most advanced models will be available through the @thewebAI application. Join the waitlist as we expand access. Proudly built in Austin, Texas. 🇺🇸

Lots of discussion out there about our next model(!), so I wanted to give an early look as soon as possible. Introducing Gemini 4 Argon! It shows frontier performance in complex workflows, cyber defense and software engineering. Teams are using it extensively at Google, from coding to quantum computing, great feedback. Here’s a look at the benchmarks:

MASSIVE reveal from Google. Its new flagship, Gemini 4 Argon, outscores GPT-6 Astra and Claude Opus 5.5 on most benchmarks. - beats GPT-6 Astra and Claude Opus 5.5 on some super important industry benchmarks. - its widest lead in legal work, 19.6% on Harvey's Legal Agent Benchmark against 6.7% for Anthropic's Claude Fable 5.1. - output limit jumps from 64K to 1M tokens, an industry-leading ceiling, - Only 3 groups have it today. the first is Google's own staff, vetted cyber defenders such as government agencies and security companies and trusted testers giving Google feedback. - Inside Google, Argon agents freed over 300 TiB of data-center memory, with 500 TiB to 1 PiB of total savings estimated, and made a Rust port of the libgav1 video decoder 2.7x faster by replacing 32K lines of SIMD code.

推出 Gemini 4 Argon——我们的全新前沿模型。 它专为编码、企业知识工作和网络安全防御等复杂工作流而构建——今天起通过我们的 Fairwind Program 向一组受信任的测试者推出。

Introducing Boreal-H3 — a video model built for ads and our next step toward recursive self-improvement in video generation. A good-looking video isn’t enough. The product has to stay the same. The actor has to stay the same. The label has to be right. And the action in the brief actually has to happen. So we post-trained MiniMax H3 specifically for advertising. But this isn’t a one-off SFT or LoRA fine-tune. We built a closed-loop system that learns what to improve next. Human-calibrated evaluation diagnoses failures and guides the next intervention: targeted data collection, reinforcement learning, or inference optimization. When the feedback is unreliable, we revise the evaluator or reward—not just the generator. Every experiment feeds into shared memory, informing the next training decision. The model improves, and so does the process that produces its successor. The results: → 85.3% reference fidelity — highest among the frontier video generation models we evaluated → Brief success: 28% → 50% → Identity match: 83% → 94% → Visible defects per clip: down 70% → Generation time and estimated cost: down 20% Boreal-H3 doesn’t just make better-looking video. It makes more usable ads. Credit to the @MiniMax_AI team for the foundation we’re building on. This launch is a checkpoint, not the finish line. We’re building more than a better video model. We’re building a system that learns how to make the next one better.

StepFun and ACE Studio present StepAudio 3 Music, StepFun’s first music generation foundation model — built to turn a prompt and lyrics into a complete song. Describe the sound you want: • Genre, mood and vocal character • Instruments, key and BPM • Song structure and arrangement Four workflows in one model: 🎤 Song generation 🎹 Instrumental generation 🔁 Music cover 🎙️ Vocal-to-song arrangement Powered by ABC-COT, it plans musical structure and arrangement before synthesis. Generate a version, rewrite the prompt, edit the ABC notation, and iterate toward the sound in your head. (ABC-COT API coming soon) Try the interactive demo: › https://static.stepfun.com/blog/stepaudio3/music/

GPT-6.1 Sol 来了。 升级后具备更强的智能体编程和计算机使用能力，接近 Astra 的性能，缓存输入价格为标准输入定价的 95% 折扣。 GPT-6.1 Sol 专为复杂重构、深度代码库调查以及跨应用的长时间运行智能体而打造。

We are releasing Dyna-2.1, the first Physical Agent that achieves reliable super long-horizon whole-body autonomy. It combines our brand-new semi-humanoid hardware with an agentic system built around Dyna-2 to handle ultra-long real-world workflows. Here is an uncut footage of Dyna-2.1 completing an entire hour-long laundry room workflow, just like a human does.

Introducing 6.1 Sol, near Astra intelligence at one fifth of the price of Astra and 95% cache read discount. It is an absolute workhorse. Combined with ultrafast for 8X speeded available today for Astra and coming soon for 6.1 Sol.

GPT-6.1 Sol：以五分之一的价格提供接近 Astra 的智能。 它是当今可用的、在其性能水平上最具成本效益的模型。