Gemini 4 Argon: Built to solve your hardest problems

來源原標題

Gemini 4 Argon: Built to solve your hardest problems

機構

Google DeepMind

原始連結

https://deepmind.google/blog/introducing-gemini-4-argon/

摘要原文

Gemini 4 Argon: Built to solve your hardest problems Oct 13, 2026 | We’re giving developers, enterprises, scientists, and consumers access to our latest frontier model. Koray Kavukcuoglu SVP, Google DeepMind and Chief AI Architect, Google In this article Gemini 4 Argon is now globally available today for developers and enterprises via the API in AI Studio and Gemini platform , for Google AI Ultra subscribers in Google Antigravity , and Google AI Ultra and Pro subscribers in the Gemini app and AI Mode in Search . Argon excels at deep work in complex, real-world tasks across domains including coding, finance, legal, cybersecurity, and science. Its depth and precision across multi-step tasks leads Argon to outperform other frontier models on benchmarks across domains , ranking #1 on the Vals Index , which measures model performance across critical economic sectors like finance, coding, legal, and tax work. The model launches today at an introductory price 1 of $2 per million input tokens and $10 per million output tokens (with cached input tokens at 95% off). Teams at Google, across enterprises, and in the research community have already been using Argon to land significant impact across fields: To solve major engineering problems like large codebase refactoring and optimizations To optimize business workflows across domains like legal, finance, and science To find and patch critical cybersecurity vulnerabilities To enable novel breakthroughs in quantum computing, nuclear fusion, and life sciences Solving complex engineering tasks At Google, we’ve been using Argon on 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 code changes at a time , Argon helped the team finish an architectural refactor across hundreds of files, completing months of work in weeks. For Chrome's billions of global users, this will unlock the ability for live webpages to move fluidly between tabs, side panels, and floating windows without reloading, eliminating an entire category of browser crashes. Optimizing a production Python codebase in hours: Argon in Antigravity is helping the team behind Google's open-source Agent Development Kit (ADK), a framework powering enterprise agents, push their performance engineering further. The team gave Argon their .agents/skills rulebook and a one-paragraph goal to eliminate framework overhead without breaking existing apps. Working autonomously overnight, Argon surfaced optimizations, now live in google/adk-python , that cut in-memory session prep time by 40% in long sessions and sped up cold imports by 21%. Read the engineering deep dive to learn more. Quantum chemistry open-source package optimization: xTB (Semiempirical Extended Tight-Binding) is one of the most-cited open-source quantum chemistry packages for molecules. Scientists use it to predict molecules' 3D shapes and to screen large numbers of drug and materials candidates. Using Argon in Antigravity, our researchers made xTB up to 2.3x faster at predicting molecular structures and 1.8x faster at conformer search. The changes have been contributed to the official xTB repository on GitHub . Making our open Gemma models nearly three times as fast: Using Argon, we achieved a 2.85x end-to-end inference speedup on an A100 via vLLM (accelerating throughput from ~37,900 to over 108,000 tokens per second on Gemma 4 E2B) with 100% token parity and zero quality drop in just a few days, a process that would usually take months. Check out the upstream pull requests (optimizing everything from attention tuning to speculative decoding) to see the exact changes. Delivering complex 3D visualizations: Argon brings major improvements in spatial reasoning, mathematical precision, and visual coding compared to previous Gemini models. Using slash commands like /goal and prompting for multi-agent loops with custom skills in Antigravity, Googlers are using Argon to prototype complex 3D visualizations, high-fidelity games, and interactive animations in vanilla code that can be rendered directly in the browser. Argon built a highly detailed model of a Pagoda using multi-agent visual critique loops in Antigravity. The model autonomously sculpted a multi-building temple complex with photorealistic architecture, foliage and lighting in Blender. Argon turned pictures of a real desk into a playable miniature racing game using a multi-agent loop in Antigravity. The desk objects, room and race track were modeled and collision-checked using Blender MCP, textured with Nano Banana, and wired with physics, custom shaders, and car engine audio from Gemini Omni Flash in Three.js. Try it now in Google AI Studio. Argon in Antigravity wrote Python scripts to procedurally sculpt, rig, animate, and PBR-bake a complex 3D game using multi-agent visual critique loops then wired it for the web in Three.js with GLSL shaders, shatter physics, and a single-player AI. Try it now in Google AI Studio. With zero external libraries and 100% pure Python, Argon wrote its own 3D ray-tracing engine with a custom video tool and audio mixer to build and document a sunlit library from scratch, producing, narrating, and assembling this video with Gemini 3.8 TTS voiceover and music by Lyria 3.5. View the source code . Starting from a single Nano Banana image in Antigravity, Argon drives Blender MCP in an autonomous loop, aligning camera grids, sculpting Gothic architecture, and staging audio-reactive lighting, to create a finished 3D scene. Transforming critical business and professional workflows Argon combines frontier reasoning, deep visual perception, expanded domain knowledge, and stronger writing capabilities across legal, finance, science, and coding to help with high-stakes professional workflows. It communicates clearly, keeping you in the loop, and follows style instructions better than any of our previous Gemini models. Argon answers questions more directly with less hedging, doesn’t repeat itself more than necessary, and skips clichés throughout the responses. Enterprise customers and startups in our early access program are already seeing strong wins with Argon. Using its advanced reasoning and multimodal capabilities while maintaining strict grounding, Argon excels in deep multimodal document comprehension, longitudinal data analysis, and interactive analytical sandboxes. See how Argon synthesizes ~50 GB of Airborne LiDAR scans, GIS room vectors, Library of Congress CAD drawings, and NPS archives to reconstruct 3D models of Chaco Canyon, turning fragmented archaeological records into explorable digital heritage sites. Try it now in Google AI Studio. In Gemini App, Argon takes an unstructured folder full of project context and formats it into a clean proposal deck for a client using a custom skill. Gemini then drafts and emails the deck to a client while creating an automated workflow for all future incoming client requests with a scheduled task. Watch Argon synthesize data from 3 open-source repositories into a medical learning tool where you can peel back anatomical layers, inspect clinical metadata, and trigger biomechanics in a 3D exploration of the human body. Argon using Antigravity in the Gemini macOS app generates design iterations for new legs for a sofa using Nano Banana then generates a 3d printable file for one. Starting from a sketch of the measurements and a photo of the couch Argon builds the model to the correct size to fit the sofa. At Isomorphic Labs, the team is using Argon as a partner to accelerate their drug discovery and design research workflows.