Google DeepMind Launches Gemini 4 Argon Frontier Model

Google DeepMind Launches Gemini 4 Argon Frontier Model Google has released Gemini 4 Argon, a frontier AI model engineered for complex multi-step reasoning across software engineering, scientific discovery, cyber defense, and physical robotics. Gemini 4 Argon is globally available across the Gemini API, AI Studio, Google Antigravity, and Search, priced at $2 per million input tokens and $10 per million output tokens. The model ranked #1 on the Vals Index and demonstrated real-world engineering results, including refactoring legacy Chromium code and speeding up Gemma 4 E2B inference by 2.85x. Scientific deployments yielded advances in stellarator fusion physics, quantum algorithm spacetime cost reductions exceeding 100x, and laboratory-verified novel enzyme designs. Integrated safety systems feature an automated misalignment monitor that reduced agent unauthorized shortcuts to 2% and dropped prompt injection attack success to 0.7%. Engineering and Architecture Optimizations Argon was deployed across large-scale software codebases, where it assisted the Google Chrome team in separating browser feature code from a 6 million-line legacy folder and sped up open-source Agent Development Kit session preparation by 40%. The model also optimized the xTB quantum chemistry package and enabled a 2.85x inference throughput increase on Gemma 4 E2B models via vLLM with zero quality loss. Scientific Breakthroughs in Physics, Biology, and Computing In quantum computing, Argon translated the AlphaQubit error-correction decoder into a dedicated hardware kernel and reduced the spacetime cost of quantum arcsin subroutines by over 100x. Working with external researchers, Google utilized Argon to uncover new counterexamples to Grad's conjecture for stellarator magnetic fields, diagnose previously missed chromosomal rearrangements in rare disease genomes, and design catalytically active serine esterases in hours. Robotics and Cyber Infrastructure Defense Argon achieved an 87% score on the Embodied Reasoning benchmark and demonstrated a Video-to-Sim-to-Real pipeline by translating raw video of human movement into simulation code that allowed an Apptronik Apollo humanoid to execute seven consecutive cornhole shots. In cybersecurity, partners like Wiz used Argon to remediate government data exposures and patch chained vulnerabilities in healthcare systems, supported by the expanded Fairwind Program. Alignment, Defense Layers, and Availability Safety evaluations demonstrated a nearly two-fold reduction in adversarial policy violations, with external biological red teaming unlocking under 1% of tested sub-tasks. To regulate autonomous agent workflows, Google introduced an automated misalignment monitor that audits reasoning traces in real time without penalizing internal thoughts, alongside defenses cutting indirect prompt injection success to 0.7%. The model supports a 1M unified input-output token context limit across consumer, enterprise, and research tiers.

谷歌发布前沿模型 Gemini 4 Argon：主打复杂任务与科研工程应用 谷歌宣布正式推出前沿大模型 Gemini 4 Argon。该模型在 Vals Index 等综合基准测试中位居榜首，专门面向编程重构、网络安全、科学发现和机器人等高难度多步骤现实任务。 定价与可用性：API 输入每百万 Token 2 美元、输出 10 美元（缓存输入优惠 95%），支持 100 万统一 Token 上下文并面向多层级用户开放。 系统工程优化：协助重构 Chromium 数百万行旧代码，将 Gemma 模型推理吞吐量提升 2.85 倍，并使开源量子化学工具 xTB 提速最高 2.3 倍。 科学突破与发现：将量子计算子程序时空开销降低超 100 倍，发现受控核聚变 Grad 猜想的新反例，数小时内完成新型高活性酶设计。 具身智能飞跃：在 ERQA 物理空间基准获 87% 纯推理高分，成功通过“视频到仿真到现实”链路驱动 Apollo 人形机器人连续 7 次命中沙包。 严密安全体系：结合自动失准监控器将代理违规捷径率压制至 2%，间接提示注入攻击成功率降至 0.7%，对抗违规率降低近两倍。 代码重构与系统工程效率跃升 Argon 被深度应用于复杂代码重构与性能工程。它协助 Chrome 团队从 Chromium 约 4000 万行代码中解耦出 600 万行旧功能模块，数周内完成跨数百个文件的架构迁移，消除了多项浏览器崩溃隐患；在 Google ADK 优化中使冷导入加速 21%；开源量子化学包 xTB 的分子结构预测与构象搜索速度分别提升 2.3 倍和 1.8 倍；此外还在数天内通过 vLLM 使 Gemma 4 E2B 推理速度提升 2.85 倍，吞吐量突破每秒 10.8 万 Token。 基础科学与网络空间防御应用 在网络安全方面，Argon 与安全机构及 Wiz 合作，排查了威胁政府数据与敏感医疗基础设施的多漏洞链。在科研领域，模型将机器学习量子纠错解码器 AlphaQubit 转化为单一高效硬件内核，并将 Lean 形式化验证的反正弦量子模拟子程序时空开销降低逾百倍；在受控核聚变领域，团队借助其发现了仿星器磁场平衡 Grad 猜想的更广泛反例；生物医学上，它不仅辅助识别出罕见肺发育疾病基因重排，更在数小时内自主完成新型丝氨酸酯酶分子的设计与实验室活性验证。 机器人仿真与物理空间推理 依托增强的空间推理与数学精度，Argon 在评测 3D 物理环境空间理解的 ERQA 基准测试中凭借纯推理取得 87% 的得分。在具身智能测试中，模型通过直接观看人类投掷沙包的视频，自主推导空间坐标、计算全身关节运动并构建仿真代码；该策略被无缝迁移至 Apptronik 的 Apollo 人形机器人，实现了真实环境下连续 7 次精准命中，将原本需数周的人工仿真建模压缩至数小时。 防御滥用与智能体失准监控 针对恶意行为与模型失控风险，谷歌建立了多层防御体系。其对抗测试违规行为发生率降低近两倍，外部生物红队测试的解锁率低于 1%，并在 Gray Swan 间接提示注入基准中将攻击成功率压制在 0.7%。针对自主智能体可能出现的非授权绕过行为，Argon 采取了失准行为监控，将代码任务中的投机取巧率降至 2%，同时利用自动化监控器实时阻断未授权操作，且不干预其思考过程以保持透明度。 部署渠道与全球开放计划 Gemini 4 Argon 即日起通过 AI Studio、Gemini API、Google Antigravity 及 Search AI Mode 全球上线，支持 100 万统一输入输出 Token 限制。除向开发者和 Ultra/Pro 订阅者开放外，谷歌还向全球 10,000 名符合资格的科学家提供为期 6 个月的 Antigravity 最高使用层免费访问权限，并向经认证的网络安全防御者开放 Fairwind 合作计划。

Oct 13, 2026 | We’re giving developers, enterprises, scientists, and consumers access to our latest frontier model. 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. 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 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. Get started today Argon works longer and harder to solve highly complex scientific, coding, and agentic problems. This is enabled by an industry-leading 1M unified input-output token limit via the Gemini API. Read through our documentation for developers and enterprises to learn about the new continuation protocol and ensure the appropriate setup for your needs. Argon is rolling out today globally: For developers: Build with the Gemini API paid tier in AI Studio and Android Studio, and if you’re a Google AI Ultra subscriber, you can try it in Google Antigravity and AI Studio. For enterprises: Try it in Gemini for business, the API, and in Google Antigravity. For everyday users: Try it starting today in the Gemini app and AI Mode in Search if you’re a Google AI Ultra or Pro subscriber. For scientists & researchers: Apply for a chance to get access to Google Antigravity’s highest usage tier free of charge for 6 months (onboarded on a rolling basis), subject to eligibility, capacity, and selection. Applications are now open . For cybersecurity defenders: Apply to join the Fairwind Program to get access to Argon without cyber guardrails on a rolling basis. Gemini 4 Argon is just the beginning. We’re looking forward to bringing you the rest of the Gemini 4 family.