全部 AI 动态

New HiDream models just landed in vivago R1 Studio 🚀 Introducing: • HiDream-O1 Image 2.0 • HiDream-O1 Editing 1.5 • HiDream-O1 Video All three models are now available in vivago R1 Studio - bringing the latest HiDream image generation, editing, and video capabilities directly into your creative workflow. New models. New possibilities. Go make something the internet can’t ignore. 🔥

重大新闻：@GoogleDeepMind 的 Gemini 4 Argon (High) 刚刚以 1525 分在 Text Arena 中排名第 1，并以 1679 分在 Code Arena: WebDev 中排名第 8！ 此次发布以 8 美元/百万 Token 的混合价格重塑了 Text Arena 的帕累托前沿！Gemini 4 Argon (High) 现在是成本效率最高的模型，请在下方查看其在帕累托前沿上的位置。 在 Text Arena 中，Gemini 4 Argon (High) 在 Coding、Hard Prompts、Instruction Following、Longer Query 和 Creative Writing 中排名第 1。它还在所有评估的职业领域中领先，并在英语、非英语、中文和俄语中额外获得第 1 名。 该模型比排名第 2 的 Claude Opus 4.6 (High) 高出 20 分，相比 Google 上一次发布的 Gemini 3.8 Flash (High)（排名第 11）是一次巨大飞跃！ 在 Code Arena: WebDev 中，Gemini 4 Argon (High) 比 Gemini 3.8 Flash (High) 提升了 96 分，并从第 29 名升至第 8 名。 祝贺 @GoogleDeepMind 团队完成这一令人印象深刻的前沿发布！

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。

We built a new way to train contextual embedding models, which encode each chunk of a document with the whole document in view. pplx-embed-v2-context-9b-preview sets a new state of the art on ConTEB and @turbopuffer's new, privately held context-bench. https://www.perplexity.ai/hub/blog/contextual-embedding-beyond-the-gold-passage

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。

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.

Introducing Ideogram 4.5, the most precise edit model. With each edit, leading models add artifacts, pixel shifts, and color changes. Ideogram 4.5 eliminates artifact buildup, making multi-turn editing possible. Live in Ideogram, the API, and launch partners. Open weights soon.