Mistral AI released the public preview of Search Toolkit, a composable framework for production search pipelines in AI applications. It unifies ingestion, retrieval and evaluation through shared interfaces, is open-source and deploys in cloud, local or edge environments.
Mistral acquired Emmi AI and doubled down on fundamental Physics AI research for aerospace, automotive, semiconductor and energy industries. Results include AB-UPT handling 9 million surface and 140 million volume elements on one GPU, and around 30,000 CFD simulations of transonic flow around 3D wings.
Mistral released Mistral Medium 3.5, its first 128B dense model combining instruction following, reasoning and coding. Its weights are available under a modified MIT licence, with a 256k context window and self-hosting possible on a minimum of four GPUs.
Google DeepMind released Gemini for Science, launching three experimental tools on Google Labs: Hypothesis Generation based on Co-Scientist.
Google DeepMind and Singapore's government formed a national AI partnership with new projects in healthcare, research, education and climate. It explores an AI-assisted triadic-care model, uses AlphaFold and Google Earth for Southeast Asian infectious-disease research and develops a Gemma-based running assistant to help visually impaired athletes train independently.
Google Research said its open-source tools and datasets have enabled over 250,000 researchers and developers worldwide. Genomics tools DeepVariant, DeepConsensus and DeepPolisher have supported processing exomes and whole genomes from 2.5 million people; MedGemma has over 4.8 million downloads. Open Health Stack is deployed in more than ten countries, reaching over 65 million beneficiaries.
Google DeepMind released the open-source Gemma 4 family in four sizes—E2B, E4B, 26B MoE and 31B Dense—under Apache 2.0.
Mistral AI released its first text-to-speech model, Voxtral TTS, with 4B parameters and support for nine languages: English, French, German, Spanish, Dutch, Portuguese, Italian, Hindi and Arabic. It is available through the API and Mistral Studio at $0.016 per 1k characters.
Mistral AI released Mistral Small 4, the next major version in the series and its first model to combine Magistral reasoning, Pixtral multimodal capabilities and Devstral agentic coding in one model, under Apache 2.0.
Mistral AI joined NVIDIA Nemotron Coalition as a founding member. They plan to jointly develop frontier open AI models, with Mistral contributing architecture, multimodal capabilities and fine-tuning tools, and NVIDIA providing compute, model development tools and synthetic data pipelines.
Mistral AI released Leanstral, the first open-source coding agent for Lean 4, using a sparse architecture with 6B active parameters. Its weights are available under Apache 2.0, and it is integrated into Mistral vibe and the free labs-leanstral-2603 API endpoint.
Mistral built an autonomous agent on its open-source coding assistant Vibe to read Rails source files, generate or improve RSpec tests and run within CI/CD without human intervention.
Google Research released WAXAL, a large open speech dataset initially covering 27 sub-Saharan African languages spoken by more than 100 million people, under CC-BY-4.0.
Mistral released Voxtral Transcribe 2, comprising Voxtral Mini Transcribe V2 for batch transcription and Voxtral Realtime for real-time use.
Mistral AI investigated a vLLM memory leak in a disaggregated Prefill/Decode deployment of Mistral Medium 3.1. System memory grew linearly at 400 MB per minute, causing out-of-memory failures within hours. It occurred only on the decode side with graph compilation and KVCache transfer via NIXL. Heaptrack showed stable heap memory; the issue was outside the heap.
Mistral AI released the next-generation Devstral 2 coding family, including 123B Devstral 2 under a modified MIT licence and 24B Devstral Small 2 under Apache 2.0, both open-source.
Mistral released the Mistral 3 family, comprising 14B, 8B and 3B small dense models and its strongest yet Mistral Large 3, a sparse MoE with 41B active and 675B total parameters. All are open-source under Apache 2.0.
Mistral AI announced a multi-year SAP partnership to integrate its models into AI Foundation and jointly develop tailored solutions for complex European industries and the public sector. It will also accelerate vision-language-action model research with Helsing for defence and security, open a German office in the coming months and significantly expand its local team.
Mistral AI released Voxtral speech-understanding models in 24B and 3B versions, both under Apache 2.0 and available via its API. With a 32k-token context, they handle up to 30 minutes of transcription or 40 minutes of audio understanding, including built-in question answering and summaries, automatic multilingual detection and voice-triggered function calls, inheriting Mistral Small 3.1's text capabilities.
Mistral AI partnered with All Hands AI to release Devstral Medium and upgrade Devstral Small 1.1.
Mistral AI launched AI for Citizens, helping governments and public institutions strategically apply AI to transform public services, innovate and safeguard competitiveness. It offers open-source models, self-hosting, data sovereignty and tailored R&D, with government and public-sector partners in France, Luxembourg, Singapore, the Netherlands, the UK and Switzerland.
Mistral AI launched Mistral Compute, a private integrated AI infrastructure stack covering GPUs, orchestration, APIs, products and services, ranging from bare-metal servers to fully managed PaaS.
Mistral AI released its first reasoning model, Magistral, comprising the 24B open-source Magistral Small and enterprise-focused Magistral Medium.
Mistral AI released Agents API, combining its language models with built-in connectors for code execution, image generation, document libraries and web search, plus MCP tools, persistent memory across conversations and agent orchestration.
Mistral AI and All Hands AI jointly released Devstral, an agentic LLM for software engineering tasks, open-source under Apache 2.0.
Mistral AI released Mistral Small 3.1, improving text performance and multimodal understanding over Small 3, expanding context to 128k tokens and reaching 150 tokens per second, under Apache 2.0.