AI 模型和平台
Mistral的1-Trillion-Parameter Le Chonk检查站获得在Hugging Face上
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Mistral AI在2026年10月29日的Mistral Large 4的重量，其旗舰1-trillion-参数土生土长的多模式型号被称为Le Chonk，使得检查点可以从Hugging Face下载，用于自宿，并在经过修改的麻省理工学院开放重量许可下，通过API和伙伴平台提供。
发布时沿用了该模型在2026年10月6日的公开预览，当时Mistral先是推出，即1.05三亿参数的专家混合物系统。 在其公告中，Mistral 称应用了预览的反馈，同时继续扩大强化学习的规模，在编码、防御网络安全和推理效率方面产生了它所说的重大收益。 该公司将Mistral Large 4定性为编码的最佳开放量模型，称的收益继续加速，没有饱和的迹象。
许可证、定价和可用性
权重在Hugging Face上以Mistral生态系统开放-重量许可证发布，这是MIT许可证的修改版。 根据其条款，该型号可以自由使用，修改，微调，并实现商业化，一个条件是：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。
根据Mistral的示范文件，API访问价格为每百万输入令牌70美元，每百万缓存输入令牌0.07美元，每百万输出令牌2.10美元。 该型号通过Mistral API和Mistral Studio服务为mistral-大-4，也可通过OpenRouter,Mistral的Vibe代码编码助理，NVIDIA托管端点与优化的NVIDIA NIM容器，以及Microsoft Foundry获得。 Mistral 称的私人部署选项是通过Foundry为Azure开发商提供的，Microsoft的蒂娜·舒奇曼是Foundry公司副总裁，称 Microsoft是第一个将Mistral大调4带到客户的云提供商。
编码基准测试和合理模式
Mistral报告说，Mistral Lige 4领先Cognition的私人FrontierCode 2.0 基准测试，评估端对端代码质量，以及一个寄存器维护者是否会将agent的变化合并，得分为19.7%，领先于包括GLM-5.3,Kimi K3,DeepSeek V4.1 Flash在内的列出的开放量级模型。
在人工分析编码Agent指数上，Mistral报告得分为48%，描述结果与领先的开放量模型GLM-5.3有效捆绑，同时平均每任务使用47%的代币。 在Terminal-Bench 4.0上，公司报告比公众预览提升了13个百分点，达到41%，在开放量模型中占据领先地位，同时将输出符减少30%. 在Mistral引用的Surge Code盲对人偏好评价中，该模型比GLM-5.3,Kimi K3和DeepSeek V4.1 Flash更受青睐，在被评价的模型中仅排在Claude Opus 5.5之后。
该模型支持三种推理模式：低，中，高，高作为默认。 Mistral 称低模式取代了之前的"无"推理模式，适合简单的任务，而介质覆盖日常使用，高则用于最需要彻底推理的最难使用的案例，让开发者以成本进行交易性能。
网络安全成果和防护设施变化
Mistral在人工分析网络指数上报告得分为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上它们。 该型号的称公司在没有不必要的拒绝的情况下处理正当的防御性安全任务，而其内置的护栏则区分防御性和恶意攻击性任务。 Mistral报告，在与网络有关的催促下，在Jailbreak Bench, StrongREJECT和AgentHarm的恶意网络请求中，平均拒绝率为96.6%，称这是在开放模型中衡量的最高。
公告还披露，Mistral在预览后监控其直播流量中与网络安全相关的使用。 称公司观察到该型号拒绝遵守对实弹目标的黑盒攻击，但认为有可能进行真正的攻击，并正在相应修改其护栏，承诺在今后几个月内进一步更新。 橙色网络防卫等企业设计合作伙伴正在接受Mistral大4的专用变体的预览访问，该变体用于渗透测试、脆弱性评估以及红队化，但需经过审查和合同保障，将测试限制在控制环境中的自有或明确授权的系统。
公告内容包括博兹艾伦·汉密尔顿，思科，Cloudflare,CrowdStrike,Elastic,ENISA,Fortinet,NVIDIA，橙网络防卫，鲁布里克，和Zscaler等高管的评价声明。 在Booz Allen Hamilton的国家网络总裁Brad Medairy的测试显示
建筑和培训
根据模型卡，Mistral Large 4是颗粒式专家混合模型，总参数约1万亿，每令牌活性参数约520亿(490亿不包括嵌入和输出层)，有128位路由专家，每令牌活性4位，外加1位共享专家。 它支持一个100万托肯的上下文窗口，通过一个16亿参数的Pixtral式视觉编码器，接受文本和图像输入和文本输出，该编码器受过培训，可以处理可变图像大小和宽度比。 Mistral表示该型号在欧洲公司自己的数据中心3800NVIDIA Grace Blackwell GPU上自始至终接受了训练，并且本土流利地使用160多种语言，包括欧盟的每一种官方语言。
设有嵌入式DSpark投机解码草稿头的检查船，提出三个令牌的块块，Mistral测得的吞吐量最高比仅服务目标模型高1.9x；还设有针对布莱克威尔GPU的量化NVFP4检查站。 在人工分析智能指数v5上，模型卡报告得分26分，被描述为西部开放量级模型中最高，同时注意到领先的中国开放量级模型在汇总指数上得分较高。 Mistral还报告其芬奇(英语：FinWork Bench)的得分攀升到73.3%，在开放型号中位居Kimi K3的第二；浏览Comp从一个有监督的微调检查点达到84.5%，得分75%；在B3 Agent安全基准测试上的攻击阻力达到97.1%，比预览中的93.3%有所上升。
Mistral 称 Mistral 大4将作为新一代专业化优化的Mistral模型的基础，指近代的发布如Voxtral和Timerstral.
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AI Models & Platforms
Mistral’s 1-Trillion-Parameter Le Chonk Checkpoint Lands on Hugging Face
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Mistral AI on October 29, 2026 released the weights of Mistral Large 4, its flagship 1-trillion-parameter natively multimodal model known as Le Chonk, making the checkpoint downloadable from Hugging Face for self-hosting and available across its API and partner platforms under a modified MIT open-weight license.
The release follows the model’s public preview on October 6, 2026, when Mistral first introduced the 1.05-trillion-parameter mixture-of-experts system. In its announcement, Mistral said it had applied feedback from the preview while continuing to scale reinforcement learning, producing what it described as major gains in coding, defensive cybersecurity, and reasoning efficiency. The company characterized Mistral Large 4 as the best open-weight model for coding and said the gains were continuing to accelerate with no signs of saturation.
License, Pricing, and Availability
The weights are published on Hugging Face under the Mistral Ecosystem Open-Weight License, a modified version of the MIT license. Under its terms, the model is free to use, modify, fine-tune, and commercialize, with one condition: businesses operating a Model-as-a-Service offering must obtain a separate commercial license from Mistral before using the model commercially.
API access is priced at $0.70 per million input tokens, $0.07 per million cached input tokens, and $2.10 per million output tokens, according to Mistral’s model documentation. The model is served as mistral-large-4 through the Mistral API and Mistral Studio, and it is also available through OpenRouter, Mistral’s Vibe Code coding assistant, NVIDIA-hosted endpoints with an optimized NVIDIA NIM container, and Microsoft Foundry. Mistral said private deployment options are offered for Azure developers through Foundry, and Microsoft’s Tina Schuchman, corporate vice president of Foundry, said Microsoft is the first cloud provider to bring Mistral Large 4 to customers.
Coding Benchmarks and Reasoning Modes
Mistral reported that Mistral Large 4 leads Cognition’s private FrontierCode 2.0 benchmark, which assesses end-to-end code quality and whether a repository maintainer would merge an agent’s change, with a score of 19.7%, ahead of listed open-weight models including GLM-5.3, Kimi K3, and DeepSeek V4.1 Flash.
On the Artificial Analysis Coding Agent Index, Mistral reported a score of 48%, describing the result as effectively tied with the leading open-weight model, GLM-5.3, while using 47% fewer tokens per task on average. On Terminal-Bench 4.0, the company reported an improvement of 13 percentage points over the public preview to 41%, taking the lead among open-weight models while reducing output tokens by 30%. In Surge Code blind pairwise human-preference evaluations cited by Mistral, the model was preferred over GLM-5.3, Kimi K3, and DeepSeek V4.1 Flash, ranking behind only Claude Opus 5.5 among the models evaluated.
The model supports three reasoning modes: low, medium, and high, with high as the default. Mistral said the low mode replaces the previous “None” reasoning mode and is suited to simple tasks, while medium covers everyday use and high is intended for the hardest use cases requiring thorough reasoning, letting developers trade performance against cost.
Cybersecurity Results and Guardrail Changes
Mistral reported a 51% score 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. The company said the model handles legitimate defensive security tasks without unnecessary refusals while its built-in guardrails distinguish between defensive and malicious offensive tasks. Mistral reported an average refusal rate of 96.6% on malicious cyber requests across the cyber-related prompts of JailbreakBench, StrongREJECT, and AgentHarm, describing it as the highest it measures among open models.
The announcement also disclosed that Mistral monitored cybersecurity-related use in its live traffic following the preview. The company said it observed the model refusing to comply with black-box attacks against live targets, but that it sees potential for uplift to conduct real attacks and is modifying its guardrails accordingly, with further updates promised in the coming months. Enterprise design partners such as Orange Cyberdefense are receiving preview access to a specialized variant of Mistral Large 4 optimized for penetration testing, vulnerability assessment, and red-teaming, subject to vetting and contractual safeguards that restrict testing to owned or explicitly authorized systems in controlled environments.
The announcement included evaluation statements from executives at Booz Allen Hamilton, Cisco, Cloudflare, CrowdStrike, Elastic, ENISA, Fortinet, NVIDIA, Orange Cyberdefense, Rubrik, and Zscaler. Brad Medairy, president of national cyber at Booz Allen Hamilton, said testing showed the model matching the vulnerability-detection recall rates of other frontier cyber models against the firm’s internal benchmark for source-code security analysis.
Architecture and Training
According to the model card, Mistral Large 4 is a granular mixture-of-experts model with roughly 1 trillion total parameters and about 52 billion active parameters per token (49 billion excluding embeddings and output layers), with 128 routed experts, four active per token, plus one shared expert. It supports a 1-million-token context window and accepts text and image input with text output through a 1.6-billion-parameter Pixtral-style vision encoder trained to handle variable image sizes and aspect ratios. Mistral states the model was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in the company’s own datacenters in Europe and is natively fluent in more than 160 languages, including every official language of the European Union.
The checkpoint ships with an embedded DSpark speculative-decoding draft head, which proposes blocks of three tokens and which Mistral measured at up to 1.9x higher throughput than serving the target model alone; a quantized NVFP4 checkpoint targeting Blackwell GPUs is also available. On the Artificial Analysis Intelligence Index v5, the model card reports a score of 26, described as the highest among western open-weight models while noting that leading Chinese open-weight models score higher on the aggregate index. Mistral also reported its Finch (FinWorkBench) score climbing to 73.3%, second among open models to Kimi K3; BrowseComp reaching 84.5% from a supervised fine-tuning checkpoint that scored 75%; and attack resistance of 97.1% on the B3 Agent Security Benchmark, up from 93.3% in the preview.
Mistral said Mistral Large 4 will serve as the foundation for a new generation of specialized and optimized Mistral models, pointing to recent releases such as Voxtral and Timestral.
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