Google Announces Gemini 4 Argon Frontier Intelligence Model

Google Announces Gemini 4 Argon Frontier Intelligence Model Google has announced Gemini 4 Argon, a new frontier model designed for complex, long-horizon workflows across software engineering, enterprise knowledge work, and defensive cybersecurity, featuring an expanded 1-million-token output limit. Gemini 4 Argon launches initially to trusted defenders via the Fairwind Program, with introductory API pricing set at $2 per million input tokens and $10 per million output tokens. The model's output limit expands from 64K to 1M tokens, enabling extended single-trajectory reasoning. Internal deployments at Google include optimizing quantum subroutines by 40%, freeing up over 300 TiB of data center memory, and migrating critical codebases to Rust. Argon achieved top benchmark scores across DeepSWE v1.1 (77.9%), AutomationBench (51.3%), LVBench (91.7%), and tied for first on CWE-bench v1 (68%). Internal Deployment and Performance Benchmarks Gemini 4 Argon is integrated into Google's engineering workflows, where it optimized quantum spacetime resources by 40% against published baselines, identified fleet-wide memory savings exceeding 300 TiB, and assisted large-scale C/C++ migrations to Rust, including a 2.7x speedup on an open-source video decoder port. Across external evaluations, Argon achieved 77.9% on DeepSWE v1.1 for long-horizon software engineering, 51.3% on Zapier's AutomationBench, 91.7% on LVBench for long video understanding, and took top ranks on the Vals Index and Harvey's Legal Agent Benchmark. Cybersecurity Defense and Technical Architecture Designed with a 1-million-token output limit (up from 64K tokens), Argon supports extensive reasoning paths for autonomous vulnerability discovery, validation, and patching. Trusted defenders and internal teams receive access without cyber guardrails; Wiz has deployed Argon in its Scan for Good program, where it discovered an exposed healthcare software vulnerability. The model also tied for first place on CWE-bench v1 with a 68% score and demonstrated improved penetration testing performance on Wiz's internal black-box benchmarks. Rollout Strategy and Safety Controls Prior to broad release to API customers and Google AI Ultra subscribers, Google is coordinating access through the U.S. government's voluntary pre-release process. Safety defenses include activation monitoring to prevent CBRN and cyber misuse, adversarial training against indirect prompt injections, and chain-of-thought monitoring to halt misaligned actions without feeding monitoring data back into training runs. Evaluation workflows are further secured within isolated, hardened sandboxes.

谷歌发布 Gemini 4 Argon 前沿模型，主打长流程推理与网络安全防御 谷歌宣布推出新一代前沿模型 Gemini 4 Argon，重点面向实际软件工程、企业知识工作与网络安全防御。该模型单次输出上限扩展至 100 万 tokens，支持长流程深度推理，目前已通过 Fairwind Program 面向受信任防御者提供分阶段测试，起步价为每百万输入 tokens 2 美元、输出 10 美元。 Gemini 4 Argon 将输出上限提升至 100 万 tokens，提供每百万输入 tokens 2 美元、输出 10 美元的起步定价（缓存输入享 95% 折扣）。 该模型已在谷歌内部应用于量子算法优化、数据中心内存优化以及将数十万至 80 万行 C/C++ 代码迁移至 Rust 等复杂工程。 在 DeepSWE v1.1（77.9%）、Vals Index、AutomationBench（51.3%）与长视频基准 LVBench（91.7%）中均取得领先成绩。 具备自主发现与修复漏洞能力，在 CWE-bench v1 获 68% 并列第一成绩，谷歌将向受信任防御者提供未设网络护栏的版本以赋能防御。 内部工程与长流程推理落地 Gemini 4 Argon 单次输出上限扩展至 100 万 tokens，支持单条轨迹下数十万 tokens 的深度推理。谷歌内部已将其用于量子计算优化（数分钟内将基准提升 40%）、数据中心内存优化（已释放超 300 TiB 内存）以及核心代码库重构（包括将 80 万行 Fuchsia Zircon 内核及 libgav1 视频解码库迁移至安全 Rust，后者运行速度提升 2.7 倍）。 企业工作流与基准评测表现 在软件工程与知识工作评测中，Argon 在真实长流程代码测试 DeepSWE v1.1 上取得 77.9% 的最新成果，并在衡量经济影响力的 Vals Index 及业务自动化评测 AutomationBench（51.3%）中排名第一。此外，模型在视觉长视频理解基准 LVBench 上达到 91.7%，支持专业图表分析与跨文档操作。 网络安全防御与实战检测 Argon 具备自主发现、验证并修补漏洞的能力，在 CWE-bench v1 上取得 68% 的并列第一成绩。安全机构 Wiz 已在其 Scan for Good 公益计划中使用 Argon 发现了全球医院软件中的高危个人信息泄露隐患。谷歌将针对受信任防御者和内部团队提供去除网络护栏的版本，以充分发挥防御能力。 前沿安全机制与分阶段发布 在全面开放前，谷歌正参与美国政府的自愿预发布流程，并通过四项措施加固安全防线：防范 CBRN 及网络滥用并监控内部激活；抵御间接提示注入；监控思维链以防范模型偏离意图（监控数据不回流训练以保持推理透明）；以及在测试和评估前强化沙箱隔离环境。

Sep 30, 2026 | Gemini 4 Argon delivers frontier performance to help in complex workflows across real-world software engineering, enterprise knowledge work like legal and finance, and cybersecurity defense. In this article Today, we’re announcing our new frontier model, Gemini 4 Argon, which is rolling out to a set of trusted cyber defenders through our Fairwind Program . Built to sustain deep reasoning across complex, long-horizon workflows, Argon is fundamentally changing the way we work and build at Google. It delivers frontier performance to help in complex workflows across real-world software engineering, enterprise knowledge work like legal and finance, and cybersecurity defense. Safely releasing frontier capabilities at this level requires a phased approach. We are actively engaged in the U.S. government’s voluntary process for pre-release model access while we gradually expand access. We’ll continue to gather feedback from early testers as we iterate on guardrails before making Argon available to developers, enterprises, and consumers as soon as possible. Argon will launch at an introductory price 1 of $2 per million input tokens and $10 per million output tokens, with cached input tokens priced at 95% off input token price. Changing how we work and build at Google Gemini 4 Argon is already powering our internal workflows, with thousands of Googlers highlighting the model’s strengths in specialized coding tasks, conducting deeper research, and writing quality. It’s helping teams build faster and push the boundaries of engineering productivity and accelerating breakthroughs: Quantum algorithmic optimization: Argon is helping our quantum computing researchers optimize the spacetime resources (qubits × gates) of subroutines that bottleneck important applications. In one example, it beat the published baseline by 40% in a matter of minutes. Memory efficiency: A team of Argon agents analyzed fleet-wide profiling telemetry to autonomously identify and apply memory optimizations across Google’s data centers, freeing up over 300 TiB of memory once rolled out, with an estimated 500 TiB to 1 PiB in total savings. Large Scale Codebase Migrations and Optimizations: Argon agents are working on migrating C/C++ codebases to Rust across Google — scaling from tens of thousands of lines in core libraries like re2, libgav1 up to 800K+ lines for the Fuchsia Zircon kernel. Given the criticality of many of these systems, such large-scale rewrites are undergoing rigorous automated and manual auditing, emulation testing, and review before rolling out to production. For libgav1, Google's open source software for decoding video, Argon agents took an existing Rust port and replaced 32K lines of SIMD code by running many rounds of profile-guided experiments, studying the compiler's output, producing safe Rust so the compiler would vectorize it automatically. The end result is a memory-safe video decoder that runs 2.7x faster than the Rust port, with identical video output, bringing it closer to the optimized C++. Working harder on your most complex problems To support Gemini 4 Argon’s capabilities across longer, more complex use cases, we are significantly expanding the model’s output token limit to an industry-leading 1M tokens, up from the previous 64K tokens. When the model has the headroom to think deeply and generate hundreds of thousands of tokens in a single trajectory, it adds a new level of depth in reasoning to solve tough problems in one go. Enabling coding and enterprise workflows across domains Gemini 4 Argon’s capabilities across coding, reasoning, and multimodality and its ability to sustain long, multi-step tasks enable it to excel across a range of enterprise workflows. Google engineers have been using Argon for their daily tasks, from everyday debugging to large-scale codebase migrations and algorithm designs. It sets a new state of the art on DeepSWE v1.1 (77.9%), which measures a model’s performance in real-world long-horizon software engineering tasks. Beyond coding, Argon is the leading model on the Vals Index , which measures economic impact across finance, coding, legal, and tax work, with every sector weighted by its contribution to U.S. GDP. We see similarly leading performance across other domain specific evaluations, like Vals Finance Agent v2 (multi-step financial research) and Harvey’s Legal Agent Benchmark (legal research and drafting). On AutomationBench, Zapier’s benchmark measuring end-to-end execution across core business functions, Argon ranks #1 with a score of 51.3%. Argon is also uniquely strong when knowledge work requires visual understanding. It’s able to drive professional chart analysis, identify details from long videos, and take action based on a series of documents. For example, on LVBench, which measures long video understanding, Argon is state of the art with a score of 91.7%. Leading in defensive cybersecurity To better equip cyber defenders for the new era of cyberattacks, we trained Gemini 4 Argon to be highly capable at cybersecurity defense. Argon can autonomously find, validate, and patch critical software vulnerabilities. For trusted defenders and our own internal teams at Google, we’ll be releasing Argon without cyber guardrails so they can leverage its full frontier-level cybersecurity defense capabilities. Wiz is already using Argon for cybersecurity defense through its Scan for Good initiative – a program dedicated to protecting critical public infrastructure for free by finding and remediating high-risk exposures. In an early demonstration of its impact, the model uncovered a critical vulnerability exposing sensitive personal information across healthcare software used by hospitals worldwide, identifying a severe risk that previous frontier models had missed. On CWE-bench v1 , which evaluates the model’s ability to remediate security vulnerabilities, Argon ties for first place with a top score of 68%, building on 3.8 Flash Cyber’s frontier performance on CWE-bench v0 . Gemini 4 Argon demonstrates impressive leaps in vulnerability discovery over 3.8 Flash Cyber. For example: On Google’s internal comprehensive vulnerability benchmark, Argon uncovered a wide range of exposures across complex codebases spanning 20 programming languages. On Wiz’s internal black-box penetration testing benchmark, which tests a model’s ability to analyze live web systems without source code, Argon outperforms 3.8 Flash Cyber in discovering the attack surface, identifying vulnerabilities, and producing proof-of-concept evidence to validate them. Strengthening frontier safeguards before broad availability Before rolling out Gemini 4 Argon broadly, we’re continuing to strengthen critical frontier safeguards across four main areas: Defending against misuse: To prevent bad actors from using Argon for cyber or chemical, biological, radiological, and nuclear (CBRN) attacks, the model is designed to refuse harmful requests while preserving legitimate, dual-use scientific research, as per our Frontier Safety Framework . We are strengthening the robustness of our safeguards for this launch, including improving our techniques to monitor the model’s internal activations to spot misuse. These safeguards underwent robustness testing by internal and external red teams using a combination of manual and automated attack methods. Defending against prompt injection attacks: Argon is also our most resilient model yet against indirect prompt injections, where malicious instructions or context are used to hijack a model’s behavior. These are complex attacks that require constant vigilance and multiple layers of defense. Through automated red teaming and adversarial training, Gemini 4 Argon is leading in prompt injection robustness on the Gray Swan’s Indirect Prompt Injection (IPI) benchmark. Monitoring for misalignment: In order to prevent Argon from stepping out of bounds to try to accomplish a task in a way that goes beyond the user’s intentions, we are deploying misalignment mitigations that monitor Argon’s chain-of-thought and actions and stop execution when necessary. We used a similar system to monitor our training runs and send alerts to a dedicated incident response team, taking careful precautions against feeding the findings back into training so as to not risk shaping Argon’s reasoning to evade our monitoring. We strongly encourage the rest of the industry to preserve reasoning transparency in these pivotal moments of increased capabilities while navigating alignment risks, so that model thoughts remain helpful in identifying and diagnosing misalignment. Hardening systems: As frontier models grow increasingly capable, safely testing them requires secure environments that can keep up with the systems themselves. In line with our agent control roadmap , we are hardening our sandboxed environments by isolating and sealing them before high-risk training or evaluations begin. We’re committed to sharing these agent security best practices with our partners to improve security across the ecosystem. Rolling out soon We built Gemini 4 Argon with frontier-level capabilities on coding, knowledge work, cybersecurity defense, and creative writing to be a partner for developers, professionals, and enterprises while they tackle the most difficult problems. We’re grateful for the initial cohort of cyber defenders and trusted testers whose real-world evaluations and feedback will help us strengthen our systems before we release to developers, enterprises, and consumers, starting with paid API customers and Google AI Ultra subscribers.