是为编者而建，由维权者信任。
思考
目录
Mistral 发布 Mistral大4 (Le Chonk)，一种1-trillion-parameter的多式联运模型，现在已有更好的编码，网络安全，推理效率。 它引导开放重量的基准测试进行编码和防御安全，同时为成本-性能平衡提供三种推理模式。 完全开放的重量级和欧洲培训，它赋予主权AI以自定义的、自我托管的部署选项。
本月早些时候，我们发布 Mistral Large 4 (Le Chonk)，我们的旗舰1-trillion-参数，本土的多式联运模型，在公共预览中。 对预览的反应超过了我们的预期，我们感谢我们的开发者社区，早期接入的客户，以及合作伙伴的支持，合作，和反馈。
从那时起，我们把这些反馈放在工作上，同时继续扩大强化学习的规模，推动编码和防御网络安全的大量改进。 收益继续加速，没有饱和的迹象。
今天，我们要释放这个改进后的Mistral大4的重量。 新的模型还显著提高了推理效率，使开发者在降低推论成本的同时提高了业绩。
兹Mistral 大4是编码的最佳开放量级模型，在整体上属于世界最具能力的开放量级模型。 从零开始在欧洲基础设施上培训，从一开始到发布都有清晰的分界线，它为各组织提供了主权AI的可信基础。
Mistral正在定义主权AI类别，并领导开放边境。 我们致力于建设世界上最有能力的模型，并公开释放这些模型，赋予开发者，企业和机构拥有，定制和控制其AI的权力。
为造者所造。
Mistral大调4进入了公共预览，在主调编码基准测试之间取得了强劲的结果。 仅仅三个星期的额外强化学习就将其推向了开放量级编码的前列，在FrontierCode和Terminal-Bench上领先，同时将人工分析编码Agent指数上最好的开放量级模型匹配到显著更高的象征性效率。
Cognition的私人FrontierCode 2.0 基准测试评估了端对端代码质量，以及一个寄存器维护者是否会合并agent的变化，Mistral Large 4领先，得分19.7%.
推动 pareto 边疆代理编码。
Mistral大4以显著较少的代号提供前沿编码性能，使精密的编码agents更经济，可以大规模运行。
在人工分析编码Agent指数上，Mistral大4的得分为48%，与领先的开放量模型GLM-5.3有效捆绑，同时平均每项任务使用47%的代币。
收益延伸到终端编码工作流程。 在Terminal-Bench 4.0上，Mistral大4相比其公开预览改进了13个百分点，达到41%，在开放量级模型中领先，同时将输出符减少30%.
为了让开发者对性能和成本之间的这种权衡进行更大的控制，Mistral Large 4现在支持三种推理模式：
超过基准测试，Mistral 大4的编码能力通过人类偏好验证。 在Surge Code的盲对偶评价中，Mistral大4比主要开放量级模型更受青睐，包括GLM-5.3,Kimi K3和DeepSeek V4.1 Flash，在评价的模型中仅排在Claude Opus 5.5之后。
为卫士所建。
自预览以来，我们一直与主要网络安全供应商合作，在现实世界环境中评价Mistral大4，测试Mistral大4在脆弱性复制和补丁方面的优势。
Mistral Large 4在人工分析网络指数上评分51%，该指数衡量一个模型在代码库中发现弱点的能力，复制和验证这些弱点，并在不突破现有功能的情况下补补。 关键是，Mistral Large 4在没有不必要的拒绝的情况下处理这些正当的防御性安全任务。
安全情况随着能力而改善。 我们的模型内置的护卫系统区分了防御性任务和恶意、攻击性网络安全任务。 它在Cyber 基准测试上保留了强劲的性能，同时在恶意网络请求上实现了平均96.6%的拒绝率，是我们在开放模型中衡量得最高的。
以下是全球网络安全领袖们对 Mistral 大4 的看法：
在预览后的时间里，我们仔细地监测了网络安全在现场交通中的应用。 虽然我们观察到模型拒绝遵守黑盒攻击实弹目标，但我们看到有可能提升进行真正的攻击。 我们正在相应修改我们的护栏，并将在今后几个月继续更新。
我们还向橙网络防卫等企业设计伙伴提供预览访问Mistral大4的专用变体优化，用于先进的网络安全工作流程，包括渗透测试，脆弱性评估，以及红色团队化。 准入需经过严格的审查和合同保障，测试仅限于在受控制环境中拥有或明确授权的系统。
同时，客户正利用Mistral Forge来裁剪Mistral Large 4带有专有安全数据，政策，检测逻辑，以及威胁环境。 对于定义明确的网络安全任务，可以进一步优化为更小的，针对特定任务的变体，提高准确性，使安全应用更加有效。
了解更多关于网络安全的主权AI，以及如何与我们合作。
为工所建。
Mistral大4在一系列广泛的企业使用度量标准方面不断改进。
对于与财务有关的工作，我们提高了模型执行现实世界工作流程的能力，特别是那些涉及为财务和会计使用案例创建和编辑电子表格的工作流程。 这体现在我们芬奇的得分攀升到73.3%，仅次于开放型号的Kimi K3.
网络浏览是大多数知识工作的关键部分。 我们的RL管道使我们能够在浏览器Comp上达到84.5%(从一个达到75%的SFT检查站开始)。 这个带来 Mistral大4的性能比GLM-5.3或DeepSeek Flash 4.1 1017等型号要高。
一个组织部署的Agents需要安全。 agents不应该让您面对诸如系统即时过滤或恶意代码注射等威胁。 RL增加了我们对B3 Agent安全基准测试的增益，Mistral大4在预览中抵抗97.1%的攻击对93.3%的攻击。 这使得它处于OSS前沿的顶端，意思是任何拥有该模型的人都可以运行一个安全的agent而无需额外的调音。
Mistral AI的这项重大投资强化了一年多前的邮政银行选择，并强调了我们正在共同建立的长期战略伙伴关系的相关性：支持出现一个开放和主权的欧洲AI领导人。 对公共和公民银行来说，技术主权不是可选的；它是维持我们客户信任的关键先决条件。 这种开放模式的推出打开了新的机会，并使用了案例。 通过与Mistral AI合作，我们的雄心是结合人类最佳专业知识和数字技术，为客户和员工长期提供更好的经验。
Zakaria Moursli，邮政银行业务业绩、付款和数字服务执行副总裁
在行动中看到它
网络脆弱性 Trage:agent读取来袭的脆弱性报告，调查源代码和产品背景，复制一个孤立的沙盒中的缺陷，然后在补丁上写作和粘贴，直到金丝雀开采不再有效。
Agentic Webdev：在没有人类帮助的情况下，Mistral Large 4构建了轨道轨道，由大约19,000颗卫星和绕地球运转的碎片组成的3D实时地图。 它会拉动真实的跟踪数据，写作应用程序，在浏览器中使用它来捕捉自己的bug和减速，然后修正独立审查员的旗帜，直到所有要求通过。
量子化学解析器，Mistral Large 4从头写出计算electrons在分子中的位置的软件，并对照标准化学工具检查其答案。 然后围绕它构建一个3D页：选择一个分子，如苯或水，拖动滑块将其拉开，并观察electrons在键断时重新组合。
为主权而建。
随着各组织围绕其知识和工作流程建立AI，主权意味着它们保留对其所依赖的模型及其创造的智能的控制。
Mistral大4，控制从模型本身开始。 各组织有能力在自己的基础设施上检查、定制和部署这一模式。 他们可以使用并改进Mistral Large 4而无需依赖Mistral通过API服务。 Mistral大型4型机车是在欧洲从零开始研发和训练的，让各组织清楚了解它们正在开发的技术的起源。
控制还延伸到AI在组织内的运作方式。 组织可以自定义Mistral Large 4环绕其专有数据与Mistral Forge，它们可以使用Mistral Studio构建，治理，并改进由此产生的agents和应用程序。 Mistral的应用AI专家可以与客户团队合作建造和部署这些定制系统。 团队通过这项工作了解他们构建的架构和决定，以及培训和操作这些架构的方法，以便独立运行和改进他们的AI。
主权并不总是需要自我接待；有时它只需要满足你控制要求的部署。 对于更喜欢管理服务的组织，Mistral Large 4通过Microsoft Foundry提供，为Azure开发者提供私人部署选项。
Microsoft和Mistral AI共同承诺在下一代AI中扩大客户选择。 作为第一个将Mistral大调4到客户的云端提供商，Microsoft正通过Microsoft Foundry提供Mistral最新的开放量级模型，这个企业AI平台是为规模评价，部署，操作模型而建造的。 对于推行主权AI战略的组织来说，这种组合为它们的数据、基础设施和AI业务提供了更大的灵活性和控制。
Tina Schuchman，公司副总裁，创始人，Microsoft
与 Le Chonk 一起开始建设
Mistral大型4号机今天可以尝试，部署和定制。
开始于Mistral工作室，通过Mistral API，或在OpenRouter上。 为全控，从Hugging Face下载开放权重，并用vLLM或SGLang在自己的基础设施上部署。 样板卡和牌照有你需要的一切开始。
也可以在Vibe Code中尝试新模型；赋予它一个功能来构建或者在自己的寄存器中修复错误。
Mistral Lige 4也通过NVIDIA托管端点提供，作为用于服务vLLM的优化NVIDIA NIM容器。 开发者可以使用开源 NeMo AutoModel库进行定制，也可以使用开源 TensorRT LLM库进行高通量服务。
企业还可以按照自己的专有数据定制Mistral Lige 4，使用案例，以及带有Mistral Forge的护栏。 和我们的团队谈谈学习更多。
该型号可在Mistral生态系统开放-重量许可证下使用，这是经过修改的MIT许可证。 该型号可自由使用，修改，微调，并实现商业化，只有一个条件：运营Model-a-Service offer的企业必须在商业上使用该型号之前从Mistral获得单独的商业许可证。
从今天开始，Mistral大4的定价为0.70 输入 / M,0.07 缓存输入 / M,2.10 输出 / M 令牌。
我们鼓励社区对Mistral大4进行测试。 运行自己的评估，推其极限，并分享你的调查结果与我们有关X
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Built for Coders,Trusted by Defenders.
Thinking
Summary
Mistral released Mistral Large 4 (Le Chonk), a 1-trillion-parameter multimodal model, now available with improved coding, cybersecurity, and reasoning efficiency. It leads open-weight benchmarks in coding and defensive security while offering three reasoning modes for cost-performance balance. Fully open-weight and European-trained, it empowers sovereign AI with customizable, self-hosted deployment options.
Earlier this month, we released Mistral Large 4 (Le Chonk), our flagship 1-trillion-parameter, natively multimodal model, in public preview. The response to the preview has exceeded our expectations, and we're grateful to our developer community, early-access customers, and partners for their support, collaboration, and feedback.
Since then, we've put that feedback to work while continuing to scale reinforcement learning, driving massive improvements in coding and defensive cybersecurity. Gains continue to accelerate, with no signs of saturation.
Today, we're releasing the weights of this improved Mistral Large 4. The new model also features substantial gains in reasoning efficiency that give developers greater performance while lowering inference costs.
Mistral Large 4 ranks as the best open-weight model for coding and among the world's most capable open models overall. Trained from scratch on European infrastructure with clear lineage from inception to release, it gives organizations a trusted foundation for Sovereign AI
Mistral is defining the sovereign AI category and leading the open frontier. We're committed to building the world's most capable models and releasing them openly, giving developers, enterprises, and institutions the power to own, customize, and control their AI.
Built for the Builders.
Mistral Large 4 entered public preview with strong results across leading coding benchmarks. Just three weeks of additional reinforcement learning have since propelled it to the forefront of open-weight coding, taking the lead on FrontierCode and Terminal-Bench while matching the best open-weight models on the Artificial Analysis Coding Agent Index with substantially greater token efficiency.
On Cognition's private FrontierCode 2.0 benchmark, which assesses end-to-end code quality and whether a repository maintainer would merge the agent's change, Mistral Large 4 leads with a score of 19.7%.
Pushing the pareto frontier for agentic coding.
Mistral Large 4 delivers frontier coding performance with significantly fewer tokens, making sophisticated coding agents more economical to run at scale.
On the Artificial Analysis Coding Agent Index, Mistral Large 4 scores 48%, effectively tied with the leading open-weight model, GLM-5.3, while using 47% fewer tokens per task on average.
The gains extend to terminal-based coding workflows. On Terminal-Bench 4.0, Mistral Large 4 improved by 13 percentage points to 41% compared to its public preview, taking the lead among open-weight models while reducing output tokens by 30%.
To give developers greater control over this tradeoff between performance and cost, Mistral Large 4 now supports three reasoning modes:
Beyond benchmarks, Mistral Large 4's coding capabilities are validated by human preference. In Surge Code's blind pairwise evaluations, Mistral Large 4 was preferred over leading open-weight models including GLM-5.3, Kimi K3, and DeepSeek V4.1 Flash, ranking behind only Claude Opus 5.5 among the models evaluated.
Built for the Defenders.
Since its preview, we've been working with leading cybersecurity vendors to evaluate Mistral Large 4 in real-world environments to test Mistral Large 4’s strengths in vulnerability reproduction and patching.
Mistral Large 4 scores 51% on the Artificial Analysis Cyber Index, which measures a model’s ability to discover vulnerabilities in a codebase, reproduce and validate them, and patch them without breaking existing functionality. Crucially, Mistral Large 4 handles these legitimate defensive security tasks without unnecessary refusals.
Safety has improved alongside capability. Our model’s built-in guardrails distinguishes between defensive and malicious, offensive cybersecurity tasks. It retains strong performance on Cyber benchmarks, while achieving an average refusal rate of 96.6% on malicious cyber requests, the highest we measure among open models.
Here’s what global leaders in cybersecurity have to say about Mistral Large 4:
In the time since the preview, we carefully monitored cybersecurity-related use in our live traffic. Though we observed the model refusing to comply with black-box attacks against live targets, we see a potential for uplift to conduct real attacks. We are modifying our guardrails accordingly, and will continue updating them in the months to come.
We are also providing enterprise design partners such as Orange Cyberdefense, with preview access to a specialized variant of Mistral Large 4 optimized for advanced cybersecurity workflows, including penetration testing, vulnerability assessment, and red-teaming. Access is subject to rigorous vetting and contractual safeguards, with testing restricted to owned or explicitly authorized systems in controlled environments.
Meanwhile, customers are tailoring Mistral Large 4 with proprietary security data, policies, detection logic, and threat environments using Mistral Forge. For well-defined cybersecurity tasks, it can be further optimized into smaller, task-specific variants for greater accuracy, making security applications more effective.
Learn more about sovereign AI for cybersecurity and how to work with us.
Built for Work.
Mistral Large 4 has continued improving on a range of broad enterprise-use metrics.
For finance-related work, we improved the model’s ability to perform real-world workflows, especially ones involving the creation and editing of spreadsheets for finance and accounting use cases. This is reflected in our Finch scores climbing to 73.3%, second only to Kimi K3 for open models.
Web browsing is a critical part of a majority of knowledge work. Our RL pipeline enabled us to reach 84.5% on BrowseComp (starting from an SFT checkpoint that was achieving 75%). This brings Mistral Large 4 at a higher performance than models like GLM-5.3 or DeepSeek Flash 4.1 1017.
Agents deployed by an organization need to be secure. Whether deployed to internal members or external users, agents shouldn’t open you up to threats like system prompt exfiltration or malicious code injection. RL increased our gains on the B3 Agent Security Benchmark, with Mistral Large 4 resisting 97.1% of attacks vs 93.3% in Preview. This places it at the top of the OSS frontier, meaning anyone hosting the model can run a safe agent without additional tuning.
This major investment by Mistral AI reinforces the choice La Banque Postale made over a year ago and underscores the relevance of the long-term strategic partnership we are building together: supporting the emergence of an open and sovereign European AI leader. For a public and citizen bank, technological sovereignty is not optional; it is a key prerequisite for maintaining our customers' trust. The introduction of this open model unlocks new opportunities and use cases. By partnering with Mistral AI, our ambition is to combine the best of human expertise and digital technology to deliver an enhanced experience of both customers and employees over the long term
Zakaria Moursli, Executive Vice President, Operational Performance, Payments and Digital Services, La Banque Postale
See it in Action
Cyber Vulnerability Triage: an agent reads incoming vulnerability reports, investigates the source code and product context, reproduces the flaws in an isolated sandbox, then writes and iterates on a patch until a canary exploit no longer works
Agentic webdev : with no human help, Mistral Large 4 builds Orbital Trace, a live 3D map of roughly 19,000 satellites and debris pieces orbiting Earth. It pulls real tracking data, writes the app, uses it in a browser to catch its own bugs and slowdowns, then fixes what an independent reviewer flags until all requirements pass.
Quantum chemistry solver, Mistral Large 4 writes, from scratch, software that calculates where electrons sit in a molecule, and checks its answers against a standard chemistry tool. It then builds a 3D page around it: pick a molecule such as benzene or water, drag a slider to pull it apart, and watch the electrons regroup as the bond breaks.
Built for Sovereignty.
As organizations build AI around their knowledge and workflows, sovereignty means that they retain control of the models they depend on and the intelligence they create.
With Mistral Large 4, that control begins with the model itself. Organizations have the ability to inspect, customize, and deploy the model on their own infrastructure. They can use and improve Mistral Large 4 without depending on Mistral to serve it through an API. Mistral Large 4 was developed and trained from scratch in Europe, giving organizations clarity about the origins of the technology they’re building on.
Control also extends to how AI operates inside an organization. Organizations can customize Mistral Large 4 around their proprietary data with Mistral Forge, and they can build, govern, and improve the resulting agents and applications using Mistral Studio. Mistral’s Applied AI experts can work alongside customer teams to build and deploy these tailored systems. Through that work, teams understand the architecture and decisions behind what they’ve built, along with the methods used to train and operate it, so they can run and improve their AI independently.
Sovereignty does not always require self-hosting; sometimes it just requires deployments that meet your control requirements. For organizations that prefer a managed service, Mistral Large 4 is available through Microsoft Foundry, with private deployment options for Azure developers.
Microsoft and Mistral AI share a commitment to expanding customer choice in next-generation AI. As the first cloud provider to bring Mistral Large 4 to customers, Microsoft is making Mistral’s latest open-weight model available through Microsoft Foundry, an enterprise AI platform built for evaluating, deploying, and operating models at scale. For organizations advancing sovereign AI strategies, this combination offers greater flexibility and control across their data, infrastructure, and AI operations.
Tina Schuchman, Corporate Vice President, Foundry, Microsoft
Start building with Le Chonk
Mistral Large 4 is available to try, deploy, and customize today.
Get started in Mistral Studio, through the Mistral API, or on OpenRouter. For full control, download the open weights from Hugging Face and deploy on your own infrastructure with vLLM or SGLang. The model card and license have everything you need to get started.
You can also try the new model in Vibe Code; give it a feature to build or a bug to fix in your own repository.
Mistral Large 4, is also available through NVIDIA-hosted endpoints, as an optimized NVIDIA NIM container for serving with vLLM. Developers can use the open source NeMo AutoModel library for customization or the open source TensorRT LLM library for high-throughput serving.
Enterprises can also tailor Mistral Large 4 to their proprietary data, use cases, and guardrails with Mistral Forge. Talk to our team to learn more.
The model is available to use under the Mistral Ecosystem Open-Weight license, a modified MIT license. The model is free to use, modify, fine-tune, and commercialize, with a single condition: businesses that operate a Model-as-a-Service offering must obtain a separate commercial license from Mistral before using the model commercially.
Starting today, Mistral Large 4 is priced at $0.70 input / M, $0.07 cached input / M, and $2.10 output / M tokens.
We encourage the community to put Mistral Large 4 to the test. Run your own evaluations, push its limits, and share your findings with us on X
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