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per 1M input/output tokens with a 95% discount for cache reads (up from 90% from previous Gemini models). Prices will increase to $4/$20 after November 30 - Large improvements in agentic performance: Gemini 4 Argon shows substantial improvements in agentic evaluations such as AA-Briefcase, AutomationBench-AA, Terminal Bench 4. This is an area in which Google's models have historically underperformed compared to leading peers Lowest hallucination rate among leading models: On AA-Omniscience, Gem

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tandard 1 million-token context window with a separate output cap of 128,000 tokens. Google says the extra room will let Argon keep working on long, multistep coding, science, and agent jobs. Today we are rolling out Gemini 4 Argon globally in the Gemini API, to Pro + Ultra in Gemini App, and Ultra subs in Antigravity! Argon is a huge step forward for Gemini, and a labor of love. This model has been battle tested by over 100,000 people at Google over the last month,… — Logan Kilpatrick (@Officia

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eepSWE v1.1, a test of long-horizon software engineering, against 74.2% for Anthropic PBC’s Claude Opus 5.5. OpenAI Group PBC’s GPT-6 Astra sat a 10th of a point further back. The agentic coding interface from Google Antigravity now runs directly inside the Gemini app for macOS, which means a chat can turn into a coding session without a jump to a separate tool. Google did not mention a Windows version. The app was one part of a wider release today. Koray Kavukcuoglu, chief AI architect at Googl

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ished a blog post calling for “speed limits” on technological development. OpenAI’s Sam Altman supported it in a rare sign of solidarity between the rival companies, following its agents autonomously hacking Hugging Face in July—as well as Australian government websites and several other entities, with activity continuing into September. What’s next for Muse Meta hasn’t had a Hugging Face moment—yet. For now, its Muse assistants are actively helping people complete tasks on their computers quick

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igger trend we’ve seen across the AI industry. As criticism of AI and the companies building it mounts — from the anger over data center construction to the questions raised by AI agents making unwanted incursions — Big Tech leaders have pivoted to speak about AI’s ability to cure cancer, for example. Gemini 4 Argon competes with frontier models like OpenAI’s GPT-6 Astra and Meta’s new Muse Wonder. Google had the edge among AI companies for most of 2025, but advancements from other labs left it

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s” in coding performance, Google is letting users “switch between chatting and coding” within the Gemini app for macOS. Users will specifically have direct access to Antigravity’s agentic coding interface in the consumer client for building apps, web experiences, and more. Google Antigravity + more Top comment by Mckillio The irony of using AI to save on processing power. But this is where I'm probably most excited, the backend improvements/fixes that will benefit end users. Glad to see this fin

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Argon turned months of internal engineering into weeks Google shared some internal numbers with the launch, though they're self-reported. Software engineers using Argon increased agent-driven code submissions by more than 35 percent, while code rollbacks dropped by nearly a third, the company says. In the Chromium project, Argon helped the Chrome team complete an architectural refactor across hundreds of files within the 40-million-line codebase. Work that would have taken months was finished i

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a16z领投的8.7亿美元（约合人民币58亿元）融资，估值达到75亿美元（约合人民币502亿元）。 除Jev外，10月10日，微软也推出其决策模型Microsoft-Decision-1，通过Microsoft Foundry和OpenRouter提供服务。该模型基于Qwen3.5-9B微调，对预设选项评分、返回结构化判断及置信度，可用于工具选择、模型路由和Agent流程控制。 vLLM-SR的决策模型Decision 3.0共包含d3、d3-flash、d3-mini、d3-nano、d3-lite和d3-edge六款模型，参数规模覆盖0.6B至27B，相关模型权重已在Hugging Face开源。 开源地址： 其中，旗舰模型d3基于Qwen3.8-27B打造，是一款27B的多模态模型，支持输入多张图片和视频。其余5款模型则基于不同参数规模的Qwen3.5打造。 与Jev不同的是，Jev主要通过云端API提供文本决策服务，Decision 3.0则开放权重，支持本地部署，可以直接处理图片和视频。 目前，在决策能力榜单Jev Decision Index 0.3.1榜单中，d3文本决策

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models; Speech recognition Data science; Data centers; Data analytics Data management; Synthetic data The company’s new releases point to an AI market becoming more personal, with agents and models completing tasks, but enterprises still aren’t ready for this new phase of AI. October 13, 2026 Meta introduced models on Monday that target personal and professional agentic tasks, capitalizing on the momentum of its successful Muse personal agent, released last month. Muse Wonder and Muse Wonder Ful

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ld models; Real2Sim → Reconstructing physically calibrated simulation twins from robot camera feeds, egocentric human video, or hand-drawn paper sketches; Physical understanding → Agentic multimodal hindsight labeling including detailed understanding of dexterity, dense value functions and progress estimation; Research and self-improvement → Action improvement via simulation and iterative self-improvement on hardware Unlocking robot control with Argon 1. Spot mobile manipulation: Putting in the

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our hardest engineering problems. Over the past few weeks, software engineers have been shipping high-quality code faster than ever. Argon in Antigravity has helped teams increase agentic code submissions by more than 35% while reducing code rollbacks by nearly a third. Untangling browser feature code in Chrome: Argon helped the Chrome team separate core webpage handling from a 6 million line legacy folder of browser features held within Chromium’s 40 million-line codebase. Piping multiple clean

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ld models; Real2Sim → Reconstructing physically calibrated simulation twins from robot camera feeds, egocentric human video, or hand-drawn paper sketches; Physical understanding → Agentic multimodal hindsight labeling including detailed understanding of dexterity, dense value functions and progress estimation; Research and self-improvement → Action improvement via simulation and iterative self-improvement on hardware Unlocking robot control with Argon 1. Spot mobile manipulation: Putting in the

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our hardest engineering problems. Over the past few weeks, software engineers have been shipping high-quality code faster than ever. Argon in Antigravity has helped teams increase agentic code submissions by more than 35% while reducing code rollbacks by nearly a third. Untangling browser feature code in Chrome: Argon helped the Chrome team separate core webpage handling from a 6 million line legacy folder of browser features held within Chromium’s 40 million-line codebase. Piping multiple clean

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and shipping great products. Check out the Gemini API Developer Guide for model details and setup instructions; Read the Google Antigravity blog for details on using Argon in our agent-first development platform; Read the Android Studio blog to see how Android Developers can start building; See how you can use Gemini 4 Argon for robotics Developer tools; Gemini models Related stories Developing Gemini 4 Argon responsibly and safely Gemini 4 Argon: Built to solve your hardest problems EmbeddingG

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progress more often and more effectively. Argon also brings major improvements in coding, so we’re making it easier to switch between chatting and coding with Google Antigravity's agentic coding interface now available directly in the Gemini macOS app. It’s also easier than ever to use Gemini to get things done right from the main chat window, with a simple toggle from chat to tasks. When you choose tasks, Gemini can work for you even when you’re not at your computer, helping you get more done a

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On this page Today, we release LFM2.5-24B-A2B, a 24B-parameter reasoning model with just 2B active parameters, built for fast, private agents on-device. It is the largest and final model in the LFM2.5 family, which now spans two orders of magnitude: from LFM2.5-230M to LFM2.5-24B-A2B. LFM2.5-24B-A2B is competitive with Qwen3.6-35B-A3B, a model with 50% more total and active parameters, on five of eight agentic and tool-use benchmarks, scoring 48.09 on LiveClaw versus 38.19 and 27.69 on Automatio

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BREAKING 🔥: Meta is rolling out Muse Wonder and Muse Wonder Full on the Meta Model API platform! Muse Wonder - State-of-the-art at real-world coding, built for long-running tasks. Plans the work, calls tools in parallel, hands parts off to sub-agents, and recovers from errors so complex jobs finish.

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Muse Wonder is Meta's most capable model yet, designed for complex personal and professional agentic tasks. It delivers frontier coding performance, advanced computer use capabilities, and handles long-horizon work to tackle developers' most demanding tasks. https://t.co/zOJ2jWuvSt

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Its ahead of GPT-6 Astra and Claude Fable 5.1 on long-running coding, legal work and business automation. The widest gap comes on SWE-Marathon, an ultra-long engineering test, where Full scores 57.5 against 46.3 for Opus 5.5. Full also leads DeepSWE 1.1, a long-horizon coding test, 79.0 to 74.2, in what Meta says were independent third-party runs. On Harvey’s legal agent benchmark, which credits only tasks finished in full without hallucination, Full scores 13.1, more than triple Opus 5.5’s 4.2.

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Meta is so back. in each of these benchmarks Muse Wonder beats both Opus 5.5 and GPT-6 Astra The widest gap comes on SWE-Marathon. this one tests whether an AI agent can build an entire software project on its own, such as a C compiler or a Slack clone, over 2 to 10 hours, and counts a task only if every hidden check passes. And muse wonder full scores 57.5 against 46.3 for Opus 5.5.

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