Gemini 4 Argon：用来解决你最难的问题
Gemini 4 Argon今天通过AI Studio和Gemini平台的API向开发者和企业开放，为Google AI Ultra用户提供Google Antigravity，为Google AI Ultra和Pro用户提供Gemini应用和搜索的AI Mode.
Argon在包括编码、金融、法律、网络安全和科学等各个领域的复杂、现实世界任务中出色地深入开展工作。 它跨多步骤任务的深度和精度使得Argon超越了基准测试上跨域的其他前沿模型，在Vals指数上排名第1位，该指数衡量金融、编码、法律和税务等关键经济部门的模型业绩。
现今的发布型车采用1号入门价，每百万输入令牌2美元，每百万输出令牌10美元(其中缓存输入令牌为95%的下限).
Google的团队，跨企业的团队，以及研究界已经利用Argon在田间登陆的重大影响：
- 解决大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
- 寻找和弥补关键的网络安全弱点
- 促成量子计算、核聚变和生命科学方面的新突破
解决复杂的工程任务
在Google，我们一直在用Argon来解决我们最难的工程问题。 在过去的几周里，软件工程师运输高质量代码的速度比以往任何时候都快。 Antigravity的Argon帮助团队将代理代码提交量增加了35%以上，同时将代码回滚减少近三分之一。
- Chrome的Untanling浏览器功能代码：Argon帮助Chrome团队将核心网页处理从Chromium的4000万行代码库内持有的浏览器功能的~600万行遗留文件夹中分离出来。 一次接过多个清洁码的修改，阿尔贡帮助团队在数百个文件中完成一个建筑重构，在几周内完成数月的工作。 对于Chrome的数十亿全球用户来说，这将解锁现场网页在标签，侧面板之间流畅移动的能力，以及不重新加载而浮动的windows，从而消除了整个类别的浏览器崩溃。
- 优化生产 Python 代码库数小时内：Antigravity中的Argon正在帮助Google的开源 Agent Development Kit(ADK)背后的团队，一个框架动力企业agents，进一步推进他们的性能工程。 团队向Argon提供了他们的.agents/技能规则手册以及消除框架间接费用而不打破现有应用程序的一段目标。 自主工作过夜，Argon表面优化，现在居住在google/adk-python，在长会中将模拟会话的准备时间缩短40%，冷进口加速21%. 阅读工程深潜学习更多。
- 量子化学开源包件优化：xTB(Semiempical Extendation Tight-Binding)是分子最引申的开源量子化学包件之一。 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者。 利用Argon在Antigravity中，我们的研究人员在预测分子结构时将xTB制得快到2.3x，在符合者搜索时将1.8x制得快。 这些变化已被贡献给GitHub上的官方xTB寄存器。
- 使开放的Gemma型号快近三倍：利用Argon，我们通过vLLM在A100上实现了2.85x端到端的推论速度(Gemma 4 E2B上的吞吐量从~37900加速到108,000多令牌每秒)，在短短几天内100%的表示平价，零质量下降，这一过程通常需要几个月的时间。 请检查上游拉力请求(优化所有从注意力调谐到投机解码)，以查看确切的变化。
- 交付复杂的3D可视化：与以前的Gemini模型相比，Argon 带来在空间推理，数学精度和视觉编码方面有重大改进。 Googles使用像/goal这样的斜线命令并提示具有Antigravity自定义技能的多agent环路，Googles正在使用Argon来原型复杂的3D可视化，高纯度游戏，以及香草代码中的互动动画，可以在浏览器中直接渲染。
转变关键业务和专业工作流程
Argon将前沿推理，深度视觉感知，扩展领域知识，以及跨越法律，金融，科学和编码的更强的写作能力结合起来，帮助高考专业工作流程。 它清晰的沟通，让你在循环中，并且遵循样式指示比任何我们以前的Gemini模型更好。 Argon更直接地回答问题
客户企业和我们早期接入计划中的初创企业已经看到与Argon的强胜
Argon利用其先进的推理和多式联运能力，同时保持严格的定位，在深入的多式联运文件理解、纵向数据分析和交互式分析沙盒方面表现突出。
加快对研究人员和领域专家的实际影响
研究人员、工程师和安全专家已经在利用Argon推动网络防御、科学、数学和机器人的界限。 它帮助网络捍卫者和研究人员测试假说，自主审计关键代码库和正式证明，并抓住微妙的零天弱点和逻辑缺陷。
将最先进网络能力带给更多信任的维权者
从今天开始，我们开始向另外数百个Fairwind方案伙伴扩展。
Wiz已经在使用Argon来实施其"Scan for Good"倡议，该举措保护了关键基础设施，有利于公益和免费。 随着该模型的早期访问，Wiz展示了Argon对多个高吸积部门的实际世界影响：
- 确保敏感的政府数据：作为长期网络复原力方案的一部分，Argon与联邦机构合作，帮助积极主动地查明并弥补了有可能暴露敏感数据和政府基础设施的差距。 这一努力有助于防止安全部门的关键发展细节暴露，防止未经授权进入专门模式。
- 保护关键的保健基础设施：Wiz还利用Argon保护重要的保健基础设施。 通过连锁复杂漏洞，该模型帮助团队发现并快速修复一个公开文件下载绕行，该绕行暴露了主密钥并允许管理员访问。 Argon不仅发现脆弱，还主动保护数十万个人病历。
开拓科学新突破。
Argon已经在整个科学界产生了现实世界的影响——使科学家和专家能够在多个领域找到新的发现。
量子计算：Argon正在帮助我们的研究人员在量子误差校正和算法设计两方面加快进展。 为了帮助错误解码器与实时的量子硬件保持同步，Argon将内部版本的AlphaQubit，即我们的ML量子错误校正解码器，转换成一个单一的，高度优化的硬件内核——几乎是其推论吞吐量的两倍，这样我们就可以向更大的量子设备部署更大，更有能力的模型和规模，同时继续达到严格的实时阈值。 Argon也在降低量子子例程的时空成本。 对于arcsin来说，是近代量子模拟算法的一个构件，是由Argon撰写的，与公布的量子CORDIC基线相比，在Lean 削减空间时间成本上正式核实了超过100x，这是向优化量子电路的标准库迈出的一步。
-核聚变：斯特拉拉机是极其复杂的机器，代表着无限清洁聚变能量的领先候选，但优化其3D磁场却十分困难。 自1967年以来，一个被称为 " 格劳德猜想 " 的长期假说认为，要为这些领域找到准确的数学解决方案是不可能的，直到最近找到反例，包括马特·兰德曼教授(马里兰大学). 现在，在Google的一个团队与兰德曼教授一起发现了更广泛的反实例，以证明格拉德的猜想。 这些新解决方案让聚变群中可靠的基准测试来测试他们的星体模拟代码，这些是未来设计的宝贵工具。
- 罕见疾病研究：Gamow Labs的研究人员利用Argon分析罕见发育肺病的新生儿的去识别基因组。 在一个案例中，Argon指明了一个常规软件错过多年的染色体重排，并解释了为什么它没有被发现。 在从真实的临床和实验室数据衍生出来的私人Gamow实验室中，Argon在Rare Bench(57%用于罕见病诊断)和Lab Bench(52.2%用于湿实验室推理)。 这些进展表明，对面临罕见状况的家庭的诊断更快。 详见博客2。
- 酶设计：具有精确反应力的工程酶有臭名昭著的缓慢，往往要求几个月的定点模型调制和候选人筛选。 塞林酯酶(Serine esterases)是一类在靶分子上切除酯键的酶，广泛用于再循环，以及制造药品，生物燃料和洗涤剂。 我们的科学家与Argon合作设计了盐酸酯酶，针对的是基准测试分子4MU-乙酸酯，一旦其酯质债券破裂，就会在紫外线下发光。 在几个小时之内，Argon收集了相关数据，进行了模型调试，并确定了多种生物化学启发滤波器，以获得最后一套五个新颖的设计。 随后的实验室测试证实了这些设计过程中的强劲催化活动。 酶生成成功的速度和容易性表明，酶工程在生物制造，绿色化学，药物开发等领域有快速加速的潜力。
为了进一步加快研究步伐，我们免费提供1万名科学家在Google反重力系统中的最高使用水平，为期6个月(滚动上船)，但需符合资格、能力和选择条件。 申请现已开放。
加快机器人研发。
通过集合先进的视觉语义推理，深层物理理解，以及自主代码生成，Argon解锁了跨机器人堆叠的新工作流程，加速了物理AGI的进步。 能力的这一飞跃反映在Argon在健美理性(ERQA)基准测试上的性能上，该性能测试了一个模型在3D环境中理解和解决物理，空间问题的能力。 依靠纯粹的推理，阿尔贡超越所有其他前沿模型，得分达到87%.
为了证明这些行动能力，我们测试了Argon仅仅通过观看来学习一种分散的物理技能的能力。 在一次内部测试中，阿尔贡观看了人类玩玉米洞的原始视频，绘制了环境的空间坐标图，并计算了所需的全体联合运动。 然后它自主地建立了模拟代码。 当转投Apptronik的阿波罗人造物时，机器人在一次现实世界的试验中迅速连续击沉了7发子弹。
这个"视频到视频到视频到视频"的管道将数周的人工模拟模型压缩成几个小时，将普通视频转化为机器人的可伸缩，无限的训练场。 更多了解这如何加快开发者博客中机器人的发展速度。
负责任地和安全地发展阿贡
安全和安保是Google模型开发的基础。 由于Argon是一个能力很强的模型，我们与各种第三方伙伴合作，测量某些模型的能力和保障措施。 Gemini 4 Argon代表我们最全面的安全
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Gemini 4 Argon: Built to solve your hardest problems
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.
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.
Accelerating real-world impact with researchers and domain experts
Researchers, engineers, and security specialists are already using Argon to push the boundaries of cyber defense, science, math, and robotics. It’s helping cyber defenders and researchers test hypotheses, autonomously audit critical codebases and formal proofs, and catch subtle zero-day vulnerabilities and logic flaws.
Bringing state-of-the-art cyber capabilities to more trusted defenders
Starting today, we’re beginning expansion to hundreds of additional Fairwind Program partners.
Wiz is already using Argon for its "Scan for Good" initiative, which protects critical infrastructure for the public good and for free. With early access to the model, Wiz has demonstrated Argon's real-world impact across multiple high-stakes sectors:
- Securing sensitive government data: As part of a long-standing cyber resilience program and in collaboration with a federal agency, Argon helped to proactively identify and remediate a gap that risked exposing sensitive data and government infrastructure. This effort helped prevent against the exposure of critical security sector development details and unauthorized access to specialized models.
- Protecting critical healthcare infrastructure: Wiz also used Argon to protect vital healthcare infrastructure. By chaining complex vulnerabilities, the model helped the team discover and quickly fix a public file download bypass that exposed a master key and granted administrator access. Argon went beyond spotting a vulnerability to proactively safeguarding hundreds of thousands of private patient records.
Unlocking new scientific breakthroughs
Argon is already having real-world impact across the scientific community — enabling novel discovery for scientists and experts across multiple domains.
- Quantum computing: Argon is helping our researchers accelerate progress across both quantum error correction and algorithm design. To help error decoders keep pace with quantum hardware in real-time, Argon translated an internal version of AlphaQubit, our ML quantum error correction decoder, into a single, highly optimized hardware kernel — nearly doubling its inference throughput so we can deploy larger, more capable models and scale to larger quantum devices, while continuing to meet strict real-time thresholds. Argon is also reducing spacetime costs for quantum subroutines. For arcsin, a building block in recent quantum simulation algorithms, an implementation written by Argon and formally verified in Lean cuts spacetime cost by more than 100x compared to a published quantum CORDIC baseline, a step toward a standard library of optimized quantum circuits.
- Nuclear fusion: Stellarators are incredibly complex machines that represent a leading candidate for limitless clean fusion energy, but optimizing their 3D magnetic fields is famously difficult. Since 1967, a long-standing hypothesis known as “Grad’s conjecture” assumed that finding exact mathematical solutions for these fields was impossible, until recent counterexamples were found, including by Professor Matt Landreman (University of Maryland). Now, a team at Google alongside Professor Landreman has discovered a broader class of counterexamples to Grad's conjecture. These new solutions give the fusion community reliable benchmarks to test their stellarator simulations codes, which are valuable tools for future designs.
- Rare disease research: Researchers at Gamow Labs used Argon to analyze de-identified genomes from newborns with rare developmental lung disorders. In one case, Argon pinpointed a chromosomal rearrangement that routine software missed for years, and explained why it went undetected. On private Gamow Labs evals derived from real clinical and lab data, Argon topped on RareBench (57% on assistance with rare-disease diagnosis) and LabBench (52.2% on wet-lab reasoning). These advances point to faster diagnosis for families facing rare conditions. See blog 2 for more details.
- Enzyme design: Engineering enzymes with precise reactivity is notoriously slow, often demanding months of bespoke model tuning and candidate screening. Serine esterases are a class of enzymes that cut ester bonds on targeted molecules, widely used in recycling, and the manufacture of pharmaceuticals, biofuels and detergents. Our scientists worked with Argon to design serine esterases, targeting the benchmark molecule 4MU-acetate that glows under UV light once its ester bonds are broken. In a matter of hours, Argon assembled relevant data, performed model tuning and identified multiple biochemistry-inspired filters to get to a final set of five novel designs. Subsequent laboratory testing confirmed robust catalytic activity across these designs. The speed and ease of successful enzyme generation demonstrates the potential for rapid acceleration in enzyme engineering in fields such as biomanufacturing, green chemistry, and drug development.
To further advance the pace of research, we’re offering 10,000 scientists the highest usage tier in Google Antigravity free of charge for 6 months (onboarded on a rolling basis), subject to eligibility, capacity, and selection. Applications are now open.
Speeding up robotics development
By bringing together advanced visual-semantic reasoning, deep physical understanding, and autonomous code generation, Argon unlocks new workflows across the robotics stack and accelerates progress toward physical AGI. This leap in capability is reflected in Argon’s performance on the Embodied Reasoning (ERQA) benchmark, which tests a model's ability to understand and solve physical, spatial problems in 3D environments. Relying on pure reasoning, Argon outperforms all other frontier models, with an 87% score.
To demonstrate these capabilities in action, we tested Argon’s ability to learn an out-of-distribution physical skill simply by watching. In an internal test, Argon watched a raw video of a human playing cornhole, mapped the spatial coordinates of the environment, and calculated the required whole-body joint movements. It then autonomously built the simulation code. When transferred onto Apptronik’s Apollo humanoid, the robot quickly sank seven consecutive shots in a real-world trial.
This "Video-to-Sim-to-Real" pipeline compresses weeks of manual simulation modeling into a few hours, transforming ordinary videos into a scalable, infinite training ground for robotics. Learn more about how this accelerates the pace of development in robotics in our developers blog.
Developing Argon responsibly and safely
Safety and security are foundational to model development at Google. Because Argon is a highly capable model, we collaborated with a variety of third-party partners on measurement of certain model capabilities and safeguards. Gemini 4 Argon represents our most comprehensive safety an
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