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Google发布Gemini 4 Carbon模型,强化Agent与网络安全能力

Gemini 4 Carbon is now available

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全新旗舰模型发布,不仅刷新了多项工程与科研基准,还引入了多Agent协作机制和具体的安全能力数据,值得从业者关注其实际落地表现。

Gemini 4 Carbon is now available

Gemini 4 Carbon 现已推出

Nov 10, 2026

2026年11月10日

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Developers, enterprises, scientists, and consumers can now try our latest frontier model.

开发者、企业、科学家和消费者现在可以体验我们最新的尖端模型。

Koray Kavukcuoglu

SVP, Google DeepMind and Chief AI Architect, Google

Google DeepMind 高级副总裁兼 Google 首席 AI 架构师

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In this article

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Today, we’re making Gemini 4 Carbon available to developers, enterprises, scientists, and consumers. Carbon is our strongest model with frontier performance in cybersecurity capabilities and long-horizon agentic workflows across complex knowledge work, software engineering, and scientific reasoning.

今天,我们向开发者、企业、科学家和消费者推出 Gemini 4 Carbon。Carbon 是我们最强大的模型,在网络安全能力以及复杂知识工作、软件工程和科学推理领域的长期智能体工作流方面展现出前沿性能。

Two weeks ago, we released Carbon to a set of cybersecurity defenders via our Fairwind Program. Following our recent launch of Gemini 4 Argon, Carbon pushes the frontier even further. Argon remains the ideal choice for the majority of software engineering and knowledge work, while Carbon is built for tasks that test the limits of a model’s intelligence like complex, multi-step coding and debugging and open-ended scientific research and discovery — using more tokens than Argon to explore thoroughly, reason deeply, and run extended verification loops.

两周前,我们通过 Fairwind Program 向一批网络安全防御者发布了 Carbon。随着我们最近发布 Gemini 4 Argon,Carbon 进一步推动了前沿技术的发展。Argon 仍然是大多数软件工程和知识工作的理想选择,而 Carbon 专为测试模型智能极限的任务而设计,例如复杂的多步编码与调试,以及开放式科学研究与发现——它使用比 Argon 更多的 token 进行深入探索、深度推理并运行扩展的验证循环。

Across independent benchmarks designed to test model performance in real workflows, Carbon pairs frontier accuracy with practical production economics. It ranks #1 on CursorBench 4.0, Perplexity’s WANDR, and Zapier’s AutomationBench at more than 2X lower cost per task compared to the next best external model.

在旨在测试模型在实际工作流中表现的独立基准测试中,Carbon 将前沿准确率与实用的生产经济性相结合。它在 CursorBench 4.0、Perplexity 的 WANDR 以及 Zapier 的 AutomationBench 上均排名第一,与次优的外部模型相比,每项任务的成本降低了 2 倍以上。

To put Carbon’s capabilities to work on larger and complex projects, we’re previewing /swarm in Google Antigravity, turning a single prompt into a coordinated team of agents you can track in real time. Developers can assign Carbon to roles that demand the deepest reasoning, like architectural design, coordination, and auditing, while pairing it with faster, lighter models across parallel isolated workspaces and adversarial verifiers that test every change.

为了在更大、更复杂的项目中应用 Carbon 的能力,我们正在 Google Antigravity 中预览 /swarm,将一个单一提示词转化为一个可实时跟踪的智能体协调团队。开发者可以将 Carbon 分配给需要最深层次推理的角色,如架构设计、协调和审计,同时将其与在并行隔离工作空间和对抗性验证器(用于测试每一项更改)中运行的更快、更轻量的模型配对使用。

Gemini 4 Carbon is available via the API in Google AI Studio and Gemini for business for all paying developers and enterprises, at the same introductory price 1 as Gemini 4 Argon ($2 per million input tokens and $10 per million output tokens). It is also rolling out to all Google AI Ultra subscribers in Google Antigravity, the Gemini app, and AI Mode in Search.

Gemini 4 Carbon 已通过 Google AI Studio 中的 API 以及面向企业的 Gemini 服务向所有付费开发者和企业提供,其入门价格 1 与 Gemini 4 Argon 相同(每百万输入 token 2 美元,每百万输出 token 10 美元)。它也在 Google Antigravity、Gemini 应用以及搜索中的 AI Mode 中向所有 Google AI Ultra 订阅用户逐步开放。

Higher-precision knowledge work, artifact generation, and data analysis

更高精度的知识工作、工件生成和数据分析

Across legal, finance, and enterprise operations, Gemini 4 Carbon combines frontier visual understanding, reasoning, and exhaustive retrieval to synthesize grounded insights across large sets of dense documents and data. It follows strict schemas and formatting to generate polished and precise spreadsheets, presentations, and reports.

在法律、金融和企业运营领域,Gemini 4 Carbon 结合了前沿的视觉理解、推理能力和全面检索功能,以从大量密集文档和数据中综合出有据可依的见解。它遵循严格的模式和格式规范,生成精美且精确的电子表格、演示文稿和报告。

Existing benchmarks don’t adequately capture how agents reason natively over dense files with charts, images, blueprints, schematics, and tables, so we partnered with Artificial Analysis to launch the new Multimodal Knowledge Agent (MKA) benchmark that tests a model’s multimodal performance in agentic workflows. Carbon ranks #2 while being more than 6X lower in average cost per task compared to the top performing model.

现有基准测试未能充分捕捉智能体如何在包含图表、图像、蓝图、示意图和表格的密集文件中进行原生推理,因此我们与 Artificial Analysis 合作推出了新的多模态知识智能体(MKA)基准测试,以评估模型在智能体工作流中的多模态性能。Carbon 排名第二位,且其平均任务成本比表现最佳的模型低 6 倍以上。

Using Google Antigravity’s new /swarm feature, a team of Gemini 4 Carbon agents turned historical photos and engineering plates into an interactive Three.js 3D exhibition of the Eiffel Tower's 1887–1889 construction from a single prompt.

利用 Google Antigravity 的新 /swarm 功能,一组 Gemini 4 Carbon 智能体仅通过一个提示词,便将历史照片和工程铭牌转化为埃菲尔铁塔 1887–1889 年建设过程的交互式 Three.js 3D 展览。

With seven public City Council planning PDFs (206 extracted engineering drawing views) and a single prompt, a multi-agent Carbon team using Google Antigravity’s /swarm feature built an interactive 906K-triangle Three.js reconstruction of Google’s Platform 37 office across 10 overnight loops.

借助七份公开的市议会规划 PDF(提取出 206 个工程图纸视图)和一个提示词,使用 Google Antigravity /swarm 功能的 Carbon 多智能体团队在 10 次夜间循环中构建了谷歌 Platform 37 办公室的交互式 90.6 万三角面片 Three.js 重建模型。

In Google Antigravity, Gemini 4 Carbon built an interactive multiscale textile visualizer based on an academic paper, modeling a virtual-fiber finite element framework with interactive 3D yarn meshes, 110-fiber polar tracking, and dynamic stress-strain curves with minimal prompting.

在 Google Antigravity 中,Gemini 4 Carbon 基于学术论文构建了一个交互式多尺度纺织可视化器,通过少量提示词实现了虚拟纤维有限元框架建模,包括交互式 3D 纱线网格、110 根纤维极向追踪以及动态应力-应变曲线。

In pre-release evaluations, Carbon matched the performance of the best model tested on deep financial research on Hebbia’s E2E Financial Agentic evaluation, and ranked at the top out of 23 frontier model configurations on Balyasny Asset Management’s General Financial Intelligence benchmark, spanning legal expertise, agentic tool use and execution, coding and data engineering, and perception and context understanding.

在预发布评估中,Carbon 在 Hebbia 的端到端金融智能体评估(E2E Financial Agentic evaluation)的深度金融研究任务上达到了所测最佳模型的性能水平,并在 Balyasny Asset Management 的综合金融智能基准测试中位列 23 种前沿模型配置之首,该基准涵盖法律专业知识、智能体工具使用与执行、编码与数据工程以及感知与上下文理解。

Improved agentic coding, full stack development, and 3D and UI design

增强的智能体编码、全栈开发和 3D 及 UI 设计能力

Carbon performs exceptionally well on engineering tasks that require multi-agent collaboration like multi-package migrations, cross-subsystem root-cause investigations, and complex architectural redesigns. For web app development, it runs build-and-verify loops, recovers from errors, and checks both its code and the rendered UI to produce complete, working applications. Stronger visual design, spatial reasoning, and attention to environmental detail also carry over to 3D game development and creative coding.

Carbon 在处理需要多智能体协作的工程任务方面表现卓越,例如多包迁移、跨子系统根本原因调查以及复杂的架构重新设计。对于 Web 应用开发,它能够运行构建并验证循环,从错误中恢复,并检查其代码和渲染后的 UI,从而生成完整且可运行的应用程序。更强的视觉设计、空间推理以及对环境细节的关注也延伸至 3D 游戏开发和创意编程领域。

Using Google Antigravity’s /swarm feature, Carbon built an open-world 3D game where you explore Google’s Mountain View campus as a pelican riding a GBike — coordinating subagents to model 3D assets in Blender from Omni and Nano Banana references, build the browser game in Three.js with WebGL shaders, and pair procedural Web Audio sound effects with Lyria music. Try the live app.

借助 Google Antigravity 的 /swarm 功能,Carbon 构建了一个开放世界 3D 游戏,玩家在其中扮演一只骑着 GBike 的鹈鹕,探索谷歌位于山景城的园区——协调子智能体根据 Omni 和 Nano Banana 参考在 Blender 中建模 3D 资产,使用 Three.js 和 WebGL 着色器构建浏览器游戏,并将程序化 Web Audio 音效与 Lyria 音乐配对。尝试实时应用。

Paired with improved front end taste and motion design skills in Google Antigravity, Carbon reimagined the Google Merchandise Store in two prompts, proposing five art direction concepts and generating all-new product imagery with Nano Banana 2.1. Try the live app and Gemini front end design skill.

结合 Google Antigravity 中改进的前端品味和动效设计技能,Carbon 仅用两个提示词就重新设计了 Google Merchandise Store(谷歌商品商店),提出了五种艺术指导概念,并使用 Nano Banana 2.1 生成了全新的产品图像。尝试实时应用和 Gemini 前端设计技能。

Starting from a video reference generated with Gemini Omni in Google Antigravity, built a cinematic multi-shot underwater Atlantis scene. The pipeline modeled procedural ruins, volumetric caustic lighting, and water simulation in Blender before converting the full environment to Three.js for real-time browser rendering.

从 Google Antigravity 中使用 Gemini Omni 生成的视频参考出发,构建了一个电影级的多镜头水下亚特兰蒂斯场景。该流程先在 Blender 中对程序化废墟、体积焦散光照和水模拟进行建模,然后将整个环境转换为 Three.js 以实现浏览器中的实时渲染。

Using a multi-agent loop in Google Antigravity, Carbon reconstructed a playable c. 1780 Stein-Schule fortepiano with 61-key escapement physics from CAD drawings and X-ray radiographs and placed it in a 3D model of the Royal Conservatory of Music's Koerner Hall in Toronto with seat-specific spatial acoustics. Try the live app.

利用 Google Antrigavity 中的多智能体循环,Carbon 基于 CAD 图纸和 X 射线放射照片重建了一架可玩的约 1780 年 Stein-Schule 古钢琴,具备 61 键逃逸物理特性,并将其放置在多伦多皇家音乐学院 Koerner Hall 的 3D 模型中,实现了针对特定座位的空间声学效果。尝试实时应用。

Frontier cybersecurity capabilities

前沿网络安全能力

Gemini 4 Carbon for cyber delivers our strongest cybersecurity capabilities to date, giving security teams the end-to-end intelligence they need in protecting critical software. When accessed through the Fairwind Program, Carbon leads on key benchmarks across finding (SEC-bench Pro), exploiting (ExploitGym), and patching (CWE-bench v1) vulnerabilities: inspecting software, tracing how flaws can be chained together across systems, and generating verified root-cause patches.

用于网络安全的 Gemini 4 Carbon 为我们提供了迄今为止最强大的网络安全能力,使安全团队能够在保护关键软件方面获得所需的端到端情报。通过 Fairwind Program 访问时,Carbon 在查找(SEC-bench Pro)、利用(ExploitGym)和修补(CWE-bench v1)漏洞的关键基准测试中表现领先:检查软件、追踪缺陷如何在系统间串联,以及生成经过验证的根本原因补丁。

Early testers inside and outside of Google are already seeing strong results with Carbon for cyber in practice.

Google 内部和外部的早期测试人员已经在实践中看到 Carbon for cyber 取得了显著成果。

  • In Project Fortify, Google’s autonomous system spanning scanning, triage, and remediation, Carbon hit 100% recall on scanning and 95% precision on triage, on our internal benchmark. On Open Source code repository testing we see an over 25% relative improvement on Argon's already strong recall rate, surfacing previously undetected, real-world logic flaws. Bringing these gains across Google, one of the world’s largest users of open-source software, helps secure both our own systems and the broader ecosystem.
  • 在 Project Fortify(谷歌涵盖扫描、分类和修复的自主系统)中,Carbon 在我们的内部基准测试中实现了 100% 的扫描召回率和 95% 的分类精确率。在开源代码库测试中,Argon 原本强劲的召回率相对提升了超过 25%,揭示了此前未被检测到的现实逻辑缺陷。将这些收益推广至谷歌——全球最大的开源软件用户之一——有助于保障我们自身系统及更广泛生态系统的安全。

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