Mistral Large 4: “Le Chonk”

Today, we’re launching a public preview of Mistral Large 4. Unofficially ML4, very officially: le Chonk. ML4 pushes the frontier of open-weight performance. You can try the preview API today on Mistral Studio. Weights drop end of this month. 今天，我们正式发布 Mistral Large 4 的公开预览版。非正式代号为 ML4，正式名称为：le Chonk。ML4 将开放权重模型的性能推向了新的前沿。您可以立即在 Mistral Studio 上试用预览版 API。模型权重将于本月底发布。

Frontier performance

前沿性能

ML4 is a 1 trillion-parameter natively multimodal model with 49 billion active parameters. It is our largest and most capable model to date, and it continues to improve rapidly as we refine it. ML4 是一个拥有 1 万亿参数的原生多模态模型，激活参数为 490 亿。这是我们迄今为止规模最大、能力最强的模型，并且随着我们的不断优化，它仍在快速进步。

The model demonstrates exceptional performance across coding, agentic workflows, and multimodal understanding. It already achieves performance competitive with the strongest open-source models globally, while significantly outperforming any open-weight model developed in the US or Europe. On critical enterprise workloads, including cybersecurity, finance and law, we find it to be state-of-the-art among open models. In some domains such as visual grounding, it goes further still, surpassing even frontier closed models. 该模型在编程、智能体工作流和多模态理解方面表现卓越。它不仅在性能上足以与全球最强的开源模型相媲美，而且显著超越了美国或欧洲开发的任何开放权重模型。在网络安全、金融和法律等关键企业工作负载中，我们认为它是目前开放模型中的顶尖水平。在视觉定位（visual grounding）等某些领域，它甚至更进一步，超越了前沿的闭源模型。

We will release the weights by the end of the month. Until then, we are red-teaming the model in real-world settings with cybersecurity leaders, vetted partners, and state authorities, who will access the same model with reduced moderation and expanded cyber capabilities. 我们将在本月底发布模型权重。在此之前，我们正与网络安全领域的领导者、经过审查的合作伙伴以及国家相关机构合作，在真实场景中对模型进行红队测试。他们将能够访问经过调整、降低了审核限制并扩展了网络安全功能的模型版本。

Forged in Europe. Built for AI sovereignty.

欧洲锻造，为 AI 主权而生。

ML4 was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral’s own datacenters in Europe. The public preview is served on that same infrastructure. It is a significant milestone in our long-term investment across infrastructure, research, and product development: state-of-the-art performance in critical verticals, delivered through open weights, designed to give customers control over their AI. ML4 是在 Mistral 位于欧洲的自有数据中心内，使用 3,800 块 NVIDIA Grace Blackwell GPU 从零开始训练的。此次公开预览版也运行在同一基础设施上。这是我们在基础设施、研究和产品开发方面长期投入的重要里程碑：通过开放权重提供关键垂直领域的顶尖性能，旨在让客户能够掌控自己的 AI。

This is particularly important in cybersecurity, where provider-level refusals can block legitimate vulnerability research and incident response, and where losing access to a capability mid-incident can itself become a critical security risk. ML4 pairs top-tier cyber performance with open weights and self-deployment, giving organizations both the capability and the autonomy to run advanced security work under their own policies. 这一点在网络安全领域尤为重要。在这一领域，服务商层面的拒绝响应可能会阻碍合法的漏洞研究和事件响应，而在事件处理过程中失去对某种能力的访问权限本身就可能构成重大的安全风险。ML4 将顶级的网络安全性能与开放权重及自部署能力相结合，使组织既拥有执行高级安全工作的能力，又拥有根据自身策略运行的自主权。

The model will be available across multiple regions worldwide, including a European deployment that Mistral operates end-to-end, independently of other digital service providers and under European law. Fun fact: a significant share of ML4’s training data was multilingual, spanning more than 160 languages, including every official language of the European Union. 该模型将在全球多个地区提供，包括由 Mistral 端到端运营的欧洲部署版本，该版本独立于其他数字服务提供商，并受欧洲法律管辖。有趣的事实是：ML4 的训练数据中有很大一部分是多语言的，涵盖了 160 多种语言，包括欧盟的所有官方语言。

Cybersecurity

网络安全

ML4 is one of the world’s strongest AI models for cybersecurity. On the Artificial Analysis Cyber Index, an independent evaluation of how well AI models find and fix security flaws in real software, it ranks among the top five models globally and leads open-weight models developed outside China by a wide margin. ML4 是全球最强大的网络安全 AI 模型之一。在“人工智能分析网络指数”（Artificial Analysis Cyber Index）——一项评估 AI 模型在真实软件中发现并修复安全漏洞能力的独立评估中，它位列全球前五，并以巨大优势领先于中国以外开发的开放权重模型。

On one of the index’s tests, which asks a model to reproduce a real vulnerability in open-source software and then patch it, ML4 scores 82%, the highest of any model. It also solves 93% of the challenges in Cybench, a set of 40 exercises drawn from security competitions, one of the highest scores reported for an open-weight model. 在该指数的一项测试中（要求模型复现开源软件中的真实漏洞并进行修补），ML4 获得了 82% 的得分，是所有模型中的最高分。它还解决了 Cybench（一套源自安全竞赛的 40 个练习题）中 93% 的挑战，这是开放权重模型所报告的最高分数之一。

That top score reflects a practical advantage. Several leading closed models, including Claude Opus 5.5 and GPT-6 Astra, score near zero on the same test because they refuse to perform the task. Yet defending software often starts with proving that a flaw is real, exactly the kind of work safety filters in closed models can block. 这一高分反映了其实际优势。包括 Claude Opus 5.5 和 GPT-6 Astra 在内的几款领先闭源模型在同一测试中得分接近于零，因为它们拒绝执行该任务。然而，软件防御往往始于证明漏洞的真实性，而这正是闭源模型中的安全过滤器可能会拦截的工作。