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Gemini 4 Argon：专为解决您最棘手的问题而打造

我们为开发人员、企业、科学家和消费者提供了使用我们最新前沿模型 Gemini 4 Argon 的机会。

Gemini 4 Argon: Built to solve your hardest problems：评测柱状图、质量曲线与通过标记 — BENCHMARK / QUALITY#25

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来自 Google AI 的官方动态，版本与适用范围以发布方说明为准。

中文机器翻译 · 原文附后

Gemini 4 Argon：专为解决您最棘手的问题而打造

Gemini 4 Argon 现已在全球范围内通过 AI Studio 和 Gemini 平台中的 API、Google Antigravity 中的 Google AI Ultra 订阅者、Gemini 应用程序中的 Google AI Ultra 和 Pro 订阅者以及搜索中的 AI 模式向全球开放。

Argon 擅长在编码、金融、法律、网络安全和科学等领域的复杂现实任务中进行深入工作。其跨多步骤任务的深度和精度使 Argon 在跨领域基准测试中优于其他前沿模型，在 Vals 指数中排名第一，该指数衡量金融、编码、法律和税务工作等关键经济领域的模型性能。

该模型今天推出，介绍性价格为 1 每百万输入代币 2 美元，每百万输出代币 10 美元（缓存输入代币可享受 95% 的折扣）。

Google、跨企业和研究界的团队已经在使用 Argon 在各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查找并修补关键网络安全漏洞实现量子计算、核聚变和生命科学领域的新突破

解决复杂的工程任务

在谷歌，我们一直在使用氩气来解决最困难的工程问题。在过去的几周里，软件工程师以比以往更快的速度交付高质量的代码。 Antigravity 中的 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了近三分之一。

理清 Chrome 中的浏览器功能代码：Argon 帮助 Chrome 团队将核心网页处理与 Chromium 4000 万行代码库中保存的约 600 万行浏览器功能遗留文件夹分开。 Argon 一次通过管道进行多个干净的代码更改，帮助团队完成了数百个文件的架构重构，在几周内完成了数月的工作。对于 Chrome 的数十亿全球用户来说，这将解锁实时网页在选项卡、侧面板和浮动窗口之间流畅移动而无需重新加载的能力，从而消除整个类型的浏览器崩溃。

在数小时内优化生产 Python 代码库：Antigravity 中的 Argon 正在帮助 Google 开源代理开发套件 (ADK)（一个为企业代理提供支持的框架）背后的团队进一步推动其性能工程。该团队为 Argon 提供了他们的 .agents/skills 规则手册和一段目标，以在不破坏现有应用程序的情况下消除框架开销。 Argon 整夜自主工作，进行了优化，现在已在 google/adk-python 中运行，将长会话中的内存会话准备时间缩短了 40%，并将冷导入速度加快了 21%。阅读工程深度剖析以了解更多信息。量子化学开源包优化：xTB（半经验扩展紧束缚）是引用次数最多的分子开源量子化学包之一。科学家用它来预测分子的 3D 形状并筛选大量候选药物和材料。在反重力中使用氩气，我们的研究人员使 xTB 的分子结构预测速度提高了 2.3 倍，构象异构体搜索速度提高了 1.8 倍。这些更改已贡献到 GitHub 上的官方 xTB 存储库。使我们的开放 Gemma 模型速度提高近三倍：使用 Argon，我们通过 vLLM 在 A100 上实现了 2.85 倍的端到端推理加速（在 Gemma 4 E2B 上将吞吐量从每秒约 37,900 个令牌加速到超过 108,000 个令牌），并在短短几天内实现了 100% 令牌奇偶性和零质量下降，这一过程通常需要几个月的时间。查看上游拉取请求（优化从注意力调整到推测解码的所有内容）以查看确切的变化。提供复杂的 3D 可视化：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。使用 /goal 等斜杠命令并提示借助反重力中的自定义技能的多代理循环，Google 员工正在使用 Argon 来制作复杂的 3D 可视化、高保真游戏以及可直接在浏览器中呈现的普通代码中的交互式动画的原型。

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Gemini 4 Argon: Built to solve your hardest problems

We’re giving developers, enterprises, scientists, and consumers access to Gemini 4 Argon, our latest frontier model.

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

解锁新的科学突破

氩气已经在整个科学界产生了现实影响——为多个领域的科学家和专家带来了新的发现。

量子计算：Argon 正在帮助我们的研究人员加速量子纠错和算法设计方面的进展。为了帮助错误解码器实时与量子硬件保持同步，Argon 将我们的 ML 量子纠错解码器 AlphaQubit 的内部版本转换为单个高度优化的硬件内核，其推理吞吐量几乎翻倍，因此我们可以部署更大、功能更强大的模型并扩展到更大的量子设备，同时继续满足严格的实时阈值。氩气还降低了量子子程序的时空成本。对于 arcsin（最近量子模拟算法的构建块），由 Argon 编写并在 Lean 中正式验证的实现与已发布的量子 CORDIC 基线相比，将时空成本降低了 100 倍以上，这是迈向优化量子电路标准库的一步。核聚变：仿星器是极其复杂的机器，代表着无限清洁聚变能源的主要候选者，但优化其 3D 磁场是出了名的困难。自 1967 年以来，一个被称为“格拉德猜想”的长期假设认为，为这些领域找到精确的数学解是不可能的，直到最近发现了反例，包括马里兰大学教授马特·兰德曼 (Matt Landreman) 的反例。现在，谷歌的一个团队与兰德曼教授一起发现了更广泛的格雷德猜想的反例。这些新的解决方案为聚变社区提供了可靠的基准来测试他们的仿星器模拟代码，这些代码是未来设计的宝贵工具。罕见疾病研究：伽莫夫实验室的研究人员使用氩气分析了患有罕见发育性肺部疾病的新生儿的未识别基因组。在一个案例中，Argon 精确定位了一条染色体常规软件多年来错过的重新排列，并解释了为什么它未被发现。在基于真实临床和实验室数据的私人伽莫夫实验室评估中，Argon 领先于 RareBench（在罕见疾病诊断方面的帮助方面占 57%）和 LabBench（在湿实验室推理方面占 52.2%）。这些进步表明，面临罕见疾病的家庭可以更快地得到诊断。有关详细信息，请参阅博客 2。酶设计：设计具有精确反应性的酶是出了名的缓慢，通常需要数月的定制模型调整和候选筛选。丝氨酸酯酶是一类可切割目标分子上的酯键的酶，广泛用于回收以及药品、生物燃料和洗涤剂的制造。我们的科学家与 Argon 合作设计了丝氨酸酯酶，以基准分子 4MU-乙酸酯为目标，一旦酯键断裂，该分子就会在紫外线下发光。在几个小时内，Argon 收集了相关数据，进行了模型调整并确定了多个受生物化学启发的过滤器，以获得最终的五种新颖设计。随后的实验室测试证实了这些设计具有强大的催化活性。成功生成酶的速度和简便性证明了酶工程在生物制造、绿色化学和药物开发等领域快速加速的潜力。

为了进一步加快研究步伐，我们为 10,000 名科学家免费提供 6 个月的 Google Antigravity 最高使用级别（滚动加入），具体取决于资格、容量和选择。申请现已开放。

负责任、安全地开发氩气

安全和安保是 Google 模型开发的基础。由于 Argon 是一个功能强大的模型，因此我们与各种第三方合作伙伴合作测量某些模型的功能和保障措施。 Gemini 4 Argon 代表了我们迄今为止最全面的安全保障堆栈：

滥用和越狱防御：帮助防御者修补医院软件或加速生物工程的相同编码和科学功能也需要针对恶意行为者的强有力的保护措施。在我们的前沿安全框架下，我们评估了 Argon 在多个领域的增强功能，并部署了结合越狱防御、监控和执行的多层防御堆栈。这极大地提高了我们的稳健性——例如，外部生物红队只能解锁我们测试的不到 1% 的生物子任务。此外，当涉及到危险请求时——从编写漏洞代码到帮助处理危险的生物材料——Argon 在满足有害请求方面比我们之前的模型要好得多。在旨在对模型进行压力测试的对抗集上，违规行为减少了近 2 倍，也明显优于之前的 Gemini 模型。代理对齐和实时监控：随着代理成为更有能力的问题解决者，我们必须确保他们在遇到障碍时不会过于急于完成任务，走未经授权的捷径，例如权限错误或测试失败。首先，我们训练 Argon 尊重系统边界，诚实地报告负面结果，并在采取不可逆转的步骤之前暂停以供人工确认。在模型没有动力阻止的编码基准测试中，Gemini 4 Argon 上的捷径尝试下降至仅 2%，与之前的 Gemini 模型相比，这是我们的最佳性能。其次，作为深度防御层，我们构建了一个自动偏差监视器，可以读取代理的逐步推理和操作，以实时捕获和阻止意外的解决方法。我们在内部部署、能力评估和强化学习训练运行中使用了该监视器——同时严格避免在训练过程中惩罚模型的想法，使其推理保持透明和可监控。及时抵抗注入：当代理浏览网页、阅读电子邮件或检查代码时，他们可能会遇到旨在劫持其任务的隐藏恶意指令。通过对抗性训练和自动化红队，Argon 在灰天鹅间接即时注入 (IPI) 基准测试中处于领先地位，将 15 次自适应尝试的攻击成功率降低至 0.7%。

有关更多信息，请参阅 Gemini 4 Argon 模型卡。

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.

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.

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 and security stack yet:

Misuse & jailbreak defenses: The same coding and scientific capabilities that help defenders patch hospital software or accelerate bioengineering also require strong safeguards against malicious actors. Under our Frontier Safety Framework, we evaluated Argon’s enhanced capabilities across several domains, and deployed a multi-layer defense stack combining jailbreak resistance, monitoring, and enforcement. This greatly improved our robustness — for instance, external bio red teaming could only unlock less than 1% of bio sub-tasks we test for. Further, when it comes to dangerous requests — from writing exploit code to helping with dangerous biological materials — Argon is significantly better at not fulfilling harmful requests than our previous models. On an adversarial set meant to stress test the model, violations reduced nearly 2-fold — also significantly better than previous Gemini models. Agent alignment & real-time monitoring: As agents become more capable problem solvers, we have to ensure they don’t get overly eager to finish a task by taking unauthorized shortcuts when they hit a roadblock, like a permission error or a failing test.First, we trained Argon to respect system boundaries, report negative findings honestly, and pause for human confirmation before taking irreversible steps. On coding benchmarks where models have no incentive to hold back, shortcut attempts dropped to just 2% on Gemini 4 Argon, our best performance compared to previous Gemini models.Second, as a defense-in-depth layer, we built an automated misalignment monitor that reads the agent’s step-by-step reasoning and actions to catch and block unintended workarounds in real time. We used this monitor across internal deployments, capability evaluations, and reinforcement learning training runs — while strictly avoiding penalizing the model’s thoughts during training so its reasoning remains transparent and monitorable. Prompt injection resistance: When agents browse webpages, read emails, or inspect code, they can encounter hidden malicious instructions designed to hijack their task. Through adversarial training and automated red teaming, Argon leads the Gray Swan Indirect Prompt Injection (IPI) benchmark, cutting attack success across 15 adaptive attempts to 0.7%.

For more information, see the Gemini 4 Argon Model Card.

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Gemini 4 Argon：专为解决您最棘手的问题而打造

Gemini 4 Argon：专为解决您最棘手的问题而打造

解决复杂的工程任务

Gemini 4 Argon: Built to solve your hardest problems

Gemini 4 Argon: Built to solve your hardest problems

转变关键业务和专业工作流程

与研究人员和领域专家一起加速现实世界的影响

为更值得信赖的防御者带来最先进的网络功能

解锁新的科学突破

加快机器人技术发展

负责任、安全地开发氩气

今天开始

Solving complex engineering tasks

Transforming critical business and professional workflows

Accelerating real-world impact with researchers and domain experts

Bringing state-of-the-art cyber capabilities to more trusted defenders

Unlocking new scientific breakthroughs

Speeding up robotics development

Developing Argon responsibly and safely

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