Mistral 在慕尼黑开设德国中心，组建专注 Physics AI 与工业 AI 的研究团队，并计划到 2030 年建成 1 吉瓦欧洲算力。该中心将携手 BMW 开展碰撞仿真与工程 AI 合作、与 Siemens Energy 推进工业 AI 应用，并与慕尼黑工业大学（TUM）合作利用风洞设施开发汽车空气动力学数字孪生。

Mistral 与 Cloudera 宣布合作，将 Mistral 模型集成进 Cloudera 混合数据平台，企业可在私有云、公有云、本地及完全气隙环境中部署推理并保持完全控制。企业还能在受控环境中用专有数据训练定制模型，数据与模型所有权均归企业，模型基于开放权重。Cloudera 平台上客户管理的数据规模达 30 exabytes。

Mistral 与 HUMAIN 宣布战略合作，覆盖 AI 基础设施、先进模型开发与 AI 解决方案部署，初期聚焦网络安全和语音，并计划开发阿拉伯语表现强劲的前沿模型。合作规模达数亿欧元，Mistral 将探索使用 HUMAIN 数据中心基础设施，双方还将在沙特面向受监管行业制定联合市场策略。

Mistral announced general availability of Mistral Regional Endpoints, letting customers choose whether inference runs in Europe or the US. Mistral Priority Tier also entered public preview, offering custom rate limits and an availability commitment backed by an SLA.

Mistral 收购 Emmi AI，以推进面向工业工程的 Physics AI 基础研究，重点覆盖航空航天、汽车、半导体和能源等行业。其已发布成果包括 AB-UPT，可在单张 GPU 上处理 9M 表面和 140M 体积网格的原始几何数据而无需重新划分网格，以及面向大型多物理过程的端到端深度学习代理模型 NeuralDEM。

Google DeepMind released Gemma 4, which it calls its most intelligent open model yet, aimed at advanced reasoning and agentic workflows under an Apache 2.0 license. The family comes in four sizes: E2B, E4B, 26B MoE and 31B Dense. The 31B ranks 3rd among open models on the Arena AI text leaderboard, and the 26B ranks 6th.

Mistral AI released Mistral Small 4, the first Mistral model to unify Magistral reasoning, Pixtral multimodal and Devstral coding-agent abilities in a single model. It ships under the Apache 2.0 license.

Mistral AI 以创始成员身份加入 NVIDIA Nemotron Coalition，双方计划联合开发前沿开源 AI 模型，Mistral AI 提供模型架构、多模态能力与微调工具，NVIDIA 提供算力、模型开发工具和合成数据管线。

Mistral 发布 Leanstral，首个专为 Lean 4 设计的开源代码智能体，采用 6B 激活参数的稀疏架构，以 Apache 2.0 许可开放权重，并提供 Mistral vibe 智能体模式和免费 API。

Mistral built an agent on its open-source coding assistant Vibe that writes Rails RSpec tests on its own. It reads source code, generates or improves tests, checks them against style rules and coverage targets, and runs unattended in CI/CD.

Mistral released Voxtral Transcribe 2, a family of two speech-to-text models: Voxtral Mini Transcribe V2 for batch transcription and Voxtral Realtime for live use.

Mistral AI 团队在 vLLM 上排查一起内存泄漏：在 Mistral Medium 3.1、开启 graph compilation 的 Prefill/Decode 分离部署中，系统内存以每分钟 400 MB 线性增长，数小时后触发 out of memory。Heaptrack 显示堆内存稳定，泄漏发生在堆外，最终指向 NIXL 经 UCX 传输 KVCache 的环节。

Mistral AI released the Devstral 2 coding model family: the 123B Devstral 2 and the 24B Devstral Small 2, under a modified MIT license and Apache 2.0 respectively. Both are open source.

Mistral AI released the Mistral 3 family: three dense models at 14B, 8B and 3B, plus Mistral Large 3, which uses a sparse MoE architecture with 41B active and 675B total parameters. All are open-sourced under Apache 2.0.

Mistral AI 宣布与 SAP 建立多年合作伙伴关系，为其 AI Foundation 集成 Mistral 模型，并共同开发面向欧洲复杂行业与公共部门的定制方案。同时与 Helsing 合作加速面向国防与安全应用的视觉-语言-动作模型研发。Mistral AI 还将在未来数月内于德国开设办公室，并大幅扩充本地团队。

Mistral released Voxtral, a speech-understanding model in 24B and 3B versions, both open-sourced under Apache 2.0 and available via API. It supports a 32k token context, handling up to 30 minutes of transcription or 40 minutes of understanding, with built-in Q&A and summarization, multilingual recognition and voice function calling. It keeps the text abilities of Mistral Small 3.1.

Mistral AI worked with All Hands AI to launch Devstral Medium and upgrade Devstral Small 1.1.

Mistral AI 推出 AI for Citizens 协作计划，帮助各国政府和公共机构战略性地运用 AI 改造公共服务、推动创新并保障竞争力。该计划提供开放模型与产品组合、自托管或 SaaS 部署选择、数据主权保障以及定制化研发，已与法国、卢森堡、新加坡、荷兰、英国、瑞士等国政府及公共部门展开合作。

Mistral AI released Magistral, its first reasoning model, in two versions: the 24B open-source Magistral Small and the enterprise Magistral Medium.

Mistral AI released Mistral Code, an enterprise AI coding assistant that combines four models: Codestral, Codestral Embed, Devstral and Mistral Medium. It runs in the cloud, on dedicated capacity or on air-gapped local GPUs, so code stays inside the company's boundary.

Mistral AI and All Hands AI released Devstral, an agentic LLM for software engineering tasks, under the Apache 2.0 license. It scores 46.8% on SWE-Bench Verified, more than 6 points above the previous open-source state of the art.

Mistral AI released Mistral Small 3.1, which improves text performance and multimodal understanding over Mistral Small 3 and extends the context window to 128k tokens. Inference runs at 150 tokens per second, and the model is open-sourced under Apache 2.0.