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Gemini 4 Argon: Built to solve your hardest problems

Gemini 4 Argon: Built to solve your hardest problems Oct 13, 2026 | x.com Facebook LinkedIn Mail Copy link We’re giving developers, enterprises, scientists, and consumers access to our latest frontier model. Koray Kavukcuoglu SVP, Google DeepMind and Chief AI Architect, Google Share x.com Facebook LinkedIn Mail Copy link 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.

Gemini 4 Argon：专为解决您最棘手的问题而打造。2026年10月13日 | x.com Facebook LinkedIn 邮件 复制链接。我们正在向开发者、企业、科学家和消费者开放我们最新的前沿模型。Koray Kavukcuoglu，Google DeepMind 高级副总裁兼 Google 首席 AI 架构师。在本文中，Gemini 4 Argon 即日起面向全球开发者和企业开放，可通过 AI Studio 和 Gemini 平台的 API 使用；Google AI Ultra 订阅用户可在 Google Antigravity 中使用；Gemini 应用和搜索中的 AI 模式则面向 Google AI Ultra 和 Pro 订阅用户开放。

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.

Argon 擅长在编程、金融、法律、网络安全和科学等多个领域的复杂现实任务中进行深度工作。其在多步骤任务中的深度和精确度，使其在各领域的基准测试中表现优于其他前沿模型，并在衡量金融、编程、法律和税务等关键经济部门模型性能的 Vals 指数中排名第一。

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.

该模型今日发布，入门价格为每百万输入 Token 2 美元，每百万输出 Token 10 美元（缓存的输入 Token 可享 95% 折扣）。Google 内部团队、各企业以及研究界已经开始使用 Argon 在多个领域产生重大影响：解决大型代码库重构和优化等重大工程问题；优化法律、金融和科学等领域的业务工作流程；发现并修补关键的网络安全漏洞；推动量子计算、核聚变和生命科学领域的突破性进展。

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.

解决复杂的工程任务：在 Google，我们一直使用 Argon 来处理最棘手的工程问题。在过去几周里，软件工程师交付高质量代码的速度比以往任何时候都快。Antigravity 中的 Argon 帮助团队将智能体代码提交量提高了 35% 以上，同时将代码回滚率降低了近三分之一。

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.

梳理 Chrome 浏览器功能代码：Argon 帮助 Chrome 团队将核心网页处理功能从 Chromium 4000 万行代码库中约 600 万行的遗留浏览器功能文件夹中分离出来。通过一次性导入多个整洁的代码更改，Argon 帮助团队完成了跨数百个文件的架构重构，在几周内完成了原本需要数月的工作。对于 Chrome 的数十亿全球用户而言，这将使实时网页能够在标签页、侧边栏和浮动窗口之间流畅切换而无需重新加载，从而消除了一整类浏览器崩溃问题。

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.

在数小时内优化生产环境 Python 代码库：Antigravity 中的 Argon 正在帮助 Google 开源 Agent Development Kit (ADK)（一个为企业智能体提供支持的框架）背后的团队进一步提升其性能工程。团队向 Argon 提供了 .agents/skills 规则手册和一个简短的目标，即在不破坏现有应用的前提下消除框架开销。Argon 在夜间自主工作，发现了多项优化方案，现已在 google/adk-python 中上线，将长会话中的内存会话准备时间缩短了 40%，并将冷导入速度提高了 21%。阅读工程深度解析以了解更多信息。

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.

量子化学开源包优化：xTB（半经验扩展紧束缚）是分子领域引用率最高的开源量子化学软件包之一。科学家利用它来预测分子的 3D 形状并筛选大量的药物和材料候选物。通过在 Antigravity 中使用 Argon，我们的研究人员使 xTB 在预测分子结构方面的速度提高了 2.3 倍，在构象搜索方面的速度提高了 1.8 倍。这些更改已贡献给 GitHub 上的官方 xTB 存储库。

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.

使我们的开源 Gemma 模型速度提升近三倍：使用 Argon，我们在几天内通过 vLLM 在 A100 上实现了 2.85 倍的端到端推理加速（在 Gemma 4 E2B 上将吞吐量从约 37,900 token/秒提升至超过 108,000 token/秒），且保持了 100% 的 Token 一致性，质量零下降，而这一过程通常需要数月时间。查看上游 Pull Request（涵盖从注意力调优到推测解码的所有优化）以了解具体更改。

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.

交付复杂的 3D 可视化效果：与之前的 Gemini 模型相比，Argon 在空间推理、数学精度和视觉编码方面带来了重大改进。通过在 Antigravity 中使用诸如 /goal 之类的斜杠命令，并针对具有自定义技能的多智能体循环进行提示，Google 员工正在使用 Argon 以原生代码原型化复杂的 3D 可视化、高保真游戏和交互式动画，这些代码可以直接在浏览器中渲染。

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 在 Antigravity 中使用多智能体视觉评估循环构建了一个高度详细的宝塔模型。该模型在 Blender 中自主雕刻了一个包含多座建筑的寺庙群，具有照片级的建筑、植被和光照效果。Argon 在 Antigravity 中使用多智能体循环将真实书桌的照片变成了可玩的微型赛车游戏。书桌物体、房间和赛道使用 Blender MCP 进行建模和碰撞检查，使用 Nano Banana 进行纹理处理，并使用 Three.js 中的物理引擎、自定义着色器以及来自 Gemini Omni Flash 的汽车引擎音频进行连接。立即在 Google AI Studio 中尝试。

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 pr

Antigravity 中的 Argon 编写了 Python 脚本，通过多智能体视觉评估循环程序化地雕刻、绑定、动画化并进行 PBR 烘焙了一个复杂的 3D 游戏，然后使用 GLSL 着色器、破碎物理效果和单人 AI 将其连接到 Three.js 的 Web 环境中。立即在 Google AI Studio 中尝试。在没有任何外部库且 100% 纯 Python 的情况下，Argon 编写了自己的 3D 光线追踪引擎，并配备了自定义视频工具和音频混合器，从零开始构建并记录了一个阳光明媚的图书馆，通过 Gemini 3.8 TTS 配音和 Lyria 3.5 的音乐制作、解说并剪辑了这段视频。查看源代码。从 Antigravity 中的一张 Nano Banana 图片开始，Argon 在自主循环中驱动 Blender MCP，对齐摄像机网格，雕刻哥特式建筑，并布置音频响应式灯光，从而创建了一个完整的 3D 场景。转型关键业务和 pr