Google Cloud上线Gemini 4 Argon模型
Gemini 4 Argon available on Google Cloud, built to help solve your business’ hardest problems
Gemini系列全新旗舰Argon发布,针对企业级复杂多步任务与高精度场景做了专项优化,实测数据详实,值得关注。
Last week we announced that Gemini Enterprise has evolved to make it even simpler to get work done, and introduced the Gemini agent, your new universal agent for work. It's always on and ready to answer your questions. It will write code, complete longer and more complex tasks, or perform whatever you need to get the job done, all from a single prompt box. And model intelligence underpins it all.
上周,我们宣布 Gemini Enterprise 已进化,使工作完成变得更加简单,并推出了 Gemini Agent——您全新的通用工作助手。它始终在线,随时准备回答您的问题。它可以编写代码、完成更长且更复杂的任务,或执行您所需的任何操作以完成任务,所有这些均通过一个提示框即可完成。而模型智能是这一切的基石。
To enable a new frontier of work, we are making our new frontier model, Gemini 4 Argon, available on Google Cloud.
为了开启工作的新前沿,我们将我们的新一代前沿模型 Gemini 4 Argon 在 Google Cloud 上提供使用。
We trained Argon to work with both depth and precision, which allows it to maintain context across long, multi-step tasks which are reinforced with strong domain expertise along the way. Whether your team is running complex software builds, handling detailed financial and legal work, or deploying cybersecurity tools like CodeMender, Argon helps your teams maintain focus from start to finish.
我们训练 Argon 具备深度和精确性,使其能够在长流程的多步骤任务中保持上下文连贯,并在过程中辅以强大的领域专业知识。无论您的团队是在运行复杂的软件构建、处理详细的财务和法律工作,还是部署 CodeMender 等网络安全工具,Argon 都能帮助您的团队从头到尾保持专注。
Customers and partners using Gemini 4 Argon
使用 Gemini 4 Argon 的客户和合作伙伴
Four ways Argon changes how your business works
Argon 改变业务运作方式的四种途径
1. Hand off your hardest work
1. 移交最艰巨的工作
Your teams already use Gemini for business to answer questions and draft content. With Argon, they can hand off the deep, multi-step work that used to take hours or days, and get back finished work they can use.
您的团队已经使用 Gemini for Business 来回答问题并起草内容。借助 Argon,他们可以将过去需要数小时或数天才能完成的深层多步骤工作交托出去,并获得可直接使用的成品。
Argon reads long documents that mix text, tables, charts and images, and turns them into one clear output grounded in those sources. It can pull findings from many sources into a single structured answer, analyze data over time, and write stronger legal, finance, and executive documents. On long tasks, it keeps going until the job is done. We trained Argon to respect system boundaries, report negative findings honestly, and pause for confirmation before taking irreversible steps, and it tells you when it couldn't find something or finish a task.
Argon 能够阅读混合了文本、表格、图表和图片的长文档,并将其转化为基于这些来源的清晰输出。它可以从多个来源提取发现结果,整合为单一的结构化答案;分析随时间变化的数据;并撰写更优质的法律、财务和管理层文档。在处理长任务时,它会持续工作直至任务完成。我们训练 Argon 尊重系统边界,诚实地报告负面发现,并在采取不可逆步骤前暂停等待确认;当它无法找到某项信息或完成任务时,也会告知您。
Customers who tested Argon tell us it excels at turning huge, unstructured inputs into cohesive outputs.
测试过 Argon 的客户告诉我们,它在将庞大且非结构化的输入转化为连贯的输出方面表现出色。
"As software development becomes more agentic, teams need models that can support long-running, multi-step work across all stages of the development lifecycle. Gemini 4 Argon is designed to help organizations bring AI into development, security, and operations while keeping it within established enterprise workflows and controls.” – Stuart Moncada, VP, GitLab.
"随着软件开发日益趋向代理化(agentic),团队需要能够支持开发生命周期各阶段长时间运行的多步骤工作的模型。Gemini 4 Argon 旨在帮助组织将 AI 引入开发、安全和运维环节,同时将其保持在既定的企业工作流和控制范围内。" —— Stuart Moncada,GitLab 副总裁。
2. Precise, verifiable results on the work that drives your business
2. 驱动您业务的精准、可验证的结果
In finance, legal, tax, and other regulated work, calculations and data extraction need to be precise, repeatable, and verifiable.
在金融、法律、税务及其他受监管的工作中,计算和数据提取必须精确、可重复且可验证。
For financial quantitative work, Argon can help automate multi-step ledger reconciliations, spreadsheet formulas, and variance analyses. It delivers high calculation integrity, citation fidelity, and formula accuracy across quantitative models. The proof is in the numbers: Argon leads on the Vals Index, which measures economic impact across finance, coding, legal, and tax work.
对于金融量化工作,Argon 可帮助自动化多步骤账目对账、电子表格公式及差异分析。它在量化模型中提供高计算完整性、引用保真度和公式准确性。数据为证:Argon 在 Vals Index 上领先,该指数衡量金融、编程、法律和税务工作的经济影响。
In legal research, Argon can help legal teams review case law and regulatory filings to pull out verified citations and key precedents. It leads on domain-specific evaluations like Harvey's Legal Agent Benchmark for legal research and drafting, with strong citation fidelity and direct answers when helping teams evaluate complex legal precedents.
在法律研究中,Argon 可帮助法律团队审查案例法和监管备案文件,提取已验证的引用和关键先例。它在 Harvey 的法律代理基准测试等特定领域评估中表现领先,在协助团队评估复杂法律先例时具备强大的引用保真度和直接回答能力。
"Gemini 4 Argon shows how much progress Google has made on agentic legal reasoning. We saw substantial improvements over Gemini 3.1 Pro across our legal agent evaluations, particularly in corporate M&A, data privacy and cybersecurity, and EC/VC. Accuracy also held up on long-document analysis, an encouraging result for lawyers working through detailed transactional matters." – Niko Grupen, Head of Applied Research, Harvey.
“Gemini 4 Argon 展示了 Google 在代理式法律推理方面取得的巨大进展。在我们的法律代理评估中,我们观察到其相比 Gemini 3.1 Pro 有显著改进,特别是在企业并购、数据隐私与网络安全以及欧盟/英国竞争法(EC/VC)领域。在长文档分析方面,准确率也保持稳定,这对处理详细交易事项的法律律师而言是一个令人鼓舞的结果。”——Niko Grupen,Harvey 应用研究主管。
“We collaborated with Google on NowAI-Bench to help shape Gemini 4 for key enterprise work. In early access, it delivered roughly 20% improved task success than Gemini 3.8 Flash, with standout gains in HR and customer service. On the ServiceNow AI Platform, our L1 Service Desk AI specialist brings Google’s most advanced intelligence, governed and secure, turning tasks into fully autonomous work.” - Nenshad Bardoliwalla, Senior Vice President, AI Products, ServiceNow.
“我们与 Google 合作参与了 NowAI-Bench 项目,以帮助塑造面向关键企业工作的 Gemini 4。在早期访问阶段,其任务成功率比 Gemini 3.8 Flash 提升了约 20%,在人力资源和客户服务方面表现尤为突出。在 ServiceNow AI 平台上,我们的 L1 服务台 AI 专家集成了 Google 最先进的智能技术,受治理且安全可靠,将任务转化为完全自主的工作。”——Nenshad Bardoliwalla,ServiceNow 高级副总裁、AI 产品负责人。
3. Build and ship products faster
3. 更快构建和交付产品
Argon is built to handle complex, multi-step projects from start to finish. Our customers consistently ask for intelligence that can break down goals, track progress, and update multiple files at the same time.
Argon 旨在从头到尾处理复杂的多步骤项目。我们的客户一致要求具备能够分解目标、跟踪进度并同时更新多个文件的智能能力。
"Gemini 4 Argon handles complex tasks reliably, from 3D games to rich animations. In Figma Make, it helps teams turn their most ambitious, multi-step ideas into impressive prototypes.” – Loredana Crisan, Chief Design Officer, Figma.
“Gemini 4 Argon 可靠地处理复杂任务,从 3D 游戏到丰富动画皆能胜任。在 Figma Make 中,它帮助团队将他们最具雄心、多步骤的想法转化为令人印象深刻的原型。”——Loredana Crisan,Figma 首席设计官。
To see how well Argon handles this, we put it to work on our own tools. The team behind our open-source Agent Development Kit (ADK) set out to reduce framework overhead and improve code performance while maintaining full compatibility with existing applications. Working autonomously overnight within automated testing guardrails, Argon surfaced optimizations, now live in google/adk-python, that cut in-memory session prep time by 40% in long sessions and speed up cold imports by 21%. To learn more, read the engineering deep dive here.
为了检验 Argon 在此方面的表现,我们将其应用于自身的工具中。我们的开源 Agent Development Kit(ADK)背后的团队致力于在保持与现有应用完全兼容的同时,减少框架开销并提升代码性能。Argon 在自动化测试护栏内自主运行了一整夜,发现了多项优化方案,现已上线于 google/adk-python,使长会话中的内存会话准备时间减少了 40%,并将冷启动导入速度提升了 21%。欲了解更多详情,请阅读此处发布的工程深度解析文章。
Across Google, engineering teams using Argon in Google Antigravity have increased agentic code submissions by more than 35% while cutting code rollbacks by nearly a third. The Chrome team used Argon to finish an architectural refactor across hundreds of files in Chromium's 40 million-line codebase, completing months of work in weeks. Argon's improvements in spatial reasoning, mathematical precision and visual coding also make it faster to go from idea to working prototype.
在 Google 内部,使用 Argon 的 Google Antigravity 工程团队使代理式代码提交量增加了 35% 以上,同时将代码回滚率降低了近三分之一。Chrome 团队利用 Argon 在 Chromium 拥有 4000 万行代码的代码库中完成了跨数百个文件的架构重构,将原本需要数月完成的工作缩短至数周。Argon 在空间推理、数学精度和视觉编码方面的改进,也加速了从想法到可用原型的转化过程。
For more advanced algorithms, our code optimization and discovery agent AlphaEvolve uses Argon to deliver strong performance improvements across real-world enterprise workloads, including factory scheduling, logistics, game code speed-ups, and chip layout — as well as complex mathematical benchmarks, and more.
对于更高级的算法,我们的代码优化与发现代理 AlphaEvolve 利用 Argon 在实际的企业工作负载中实现了显著的性能提升,涵盖工厂调度、物流、游戏代码加速以及芯片布局等领域,同时也包括复杂的数学基准测试等场景。
To learn how to best use Argon, check out the developer's guide.
要了解如何最佳地使用 Argon,请参阅开发者指南。
4. Stronger defense for your code and business
4. 为您的代码和业务提供更强大的防护
While Argon has cyber mitigations that block offensive tasks such as pentesting and exploitation, you can still take full advantage of Argon’s defensive capabilities using CodeMender to find and fix vulnerabilities, or use Argon with your own security harness. It ties for first place on CWE-Bench v1 with a top score of 68% for remediating security vulnerabilities.
尽管 Argon 具备网络缓解机制以阻止渗透测试和利用攻击等恶意任务,您仍可通过 CodeMender 充分利用 Argon 的防御能力来查找并修复漏洞,或者将 Argon 与您自己的安全测试框架结合使用。在 CWE-Bench v1 基准测试中,Argon 以 68% 的最高安全评分并列第一,展现了其在修复安全漏洞方面的卓越能力。
"We are integrating Gemini 4 Argon into MarketingX for automated codebase security audits. In our evaluations, the model successfully identified 100% of top-priority vulnerabilities with 0% fabricated vulnerabilities across verified claims. This precision unlocks proactive, code-level security for our customers before deployment.” - Yunus Emre, Chief AI Officer of AI Lab, HubX.
“我们正在将 Gemini 4 Argon 集成到 MarketingX 中,用于自动化的代码库安全审计。在我们的评估中,该模型成功识别了 100% 的高优先级漏洞,且在已验证声明中未发现任何虚假漏洞。这种精确性为我们的客户在部署前提供了主动的、代码级别的安全保障。” —— HubX AI Lab 首席 AI 官 Yunus Emre
Security also means being able to trust Argon inside your own environment. Argon is our most resilient model yet against indirect prompt injections, where malicious instructions or context are used to hijack a model’s behavior. It leads the Gray Swan Indirect Prompt Injection benchmark, cutting attack success across 15 adaptive attempts to 0.7%. That matters when Argon reads email, documents and webpages that may contain hidden malicious instructions.
安全还意味着能够信任 Argon 在您自身环境中的表现。Argon 是我们目前最具韧性的模型,能够有效抵御间接提示注入攻击,即恶意指令或上下文被用来劫持模型行为的情况。在 Gray Swan 间接提示注入基准测试中,Argon 将 15 次自适应攻击的成功率降至 0.7%。当 Argon 读取可能包含隐藏恶意指令的电子邮件、文档和网页时,这一点尤为重要。
We are also releasing Argon without cyber guardrails to a select set of trusted defenders, which allows for testing a full breadth of cybersecurity capabilities. Trusted defenders are vetted through our Fairwind Program, a limited access initiative that provides government, critical infrastructure, and cybersecurity partners exclusive, unmitigated access to advanced models.
我们还在向一组经过筛选的可信防御者发布不带网络安全护栏的 Argon,以便全面测试其网络安全能力。可信防御者需通过我们的 Fairwind Program(公平风计划)进行审核,这是一个受限访问计划,为政府、关键基础设施以及网络安全合作伙伴提供对先进模型的独家且未加限制的访问权限。
Enterprise customers and partners in our early access program are already seeing strong wins with Argon.
我们早期访问计划中的企业客户和合作伙伴已经在 Argon 上取得了显著成果。
Business leaders discuss how they are testing and building with Gemini 4 Argon.
商业领袖们分享了他们如何测试和使用 Gemini 4 Argon 进行构建的经验。
Leaders from Amplitude, Deloitte, and Rakuten share Gemini 4 Argon results
来自 Amplitude、Deloitte 和 Rakuten 的领导分享了 Gemini 4 Argon 的成果
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更进一步:量化金融体系
看懂新闻只是起点——沿量化金融路径,把它变成能交付的工程能力