Hugging Face's fourth PRX instalment details its data strategy: mixing public and internal pretraining datasets, regenerating long image captions with a VLM and converting them for streaming training. It uses Lance for construction and filtering and MDS for streaming.
Hugging Face introduced a new kernel repository type for Kernels, along with trusted publishers and code signing. By default, only trusted publishers' kernels load; other sources require explicit trust_remote_code=True.
Mistral released Leanstral 1.5 under Apache-2.0, with 119B total and 6B active parameters and major formal verification improvements. It achieves 100% on miniF2F, solves 587 of 672 PutnamBench problems, and sets current best results of 87% on FATE-H and 34% on FATE-X.
Hugging Face and Cerebras jointly demonstrated a real-time speech-to-speech pipeline, accelerating Gemma 4 31B inference with Cerebras and combining Nvidia Parakeet speech recognition with Alibaba Qwen3TTS synthesis.
Every Eval Ever (EEE) and Hugging Face Community Evals are now interoperable, allowing cross-publication of evaluations with links to complete records. EEE stores around 229,000 results across more than 22,000 models and 2,200 benchmarks from 31 reporting formats.
Google DeepMind released experimental open-source model DiffusionGemma, generating text blocks in parallel through text diffusion and achieving up to 4 times faster inference on dedicated GPUs.
Google DeepMind released Gemma 4 12B, a multimodal model for local laptop use between the edge-focused E4B and 26B MoE. Its unified encoder-free architecture feeds visual and audio inputs directly into the LLM backbone.
Google Research open-sourced its hydrological modelling framework on GitHub under Apache 2.0, enabling national weather and hydrology agencies to integrate AI flood forecasting. The Python package uses PyTorch and an LSTM architecture, can train or fine-tune on Caravan data, and includes interactive tutorial notebooks and videos.
Search Toolkit is a composable framework for building production search pipelines for AI applications.
Published breakthroughs pushing the state of the art.
Introducing Mistral Medium 3.5, remote coding agents in Vibe, plus new Work mode in Le Chat for complex tasks.
A collection of science tools and experiments to expand the scale and precision of scientific exploration.
Google DeepMind and Singapore partner to apply frontier AI to address complex challenges across health, education, and sustainability and more.
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.
Gemma 4: Our most intelligent open models to date, purpose-built for advanced reasoning and agentic workflows.
Voxtral TTS: A frontier, open-weights text-to-speech model that’s fast, instantly adaptable, and produces lifelike speech for voice agents.
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.
At Mistral AI, we believe the future of artificial intelligence is open, collaborative, and built for everyone. Today, we’re proud to announce our participation as a founding member in the NVIDIA Nemotron Coalition, a groundbreaking global initiative uniting leading AI labs to advance open, frontier-level foundation models.
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.
We are building a multiyear partnership with SAP to deliver a fully sovereign AI stack for Germany and Europe, integrating our models into SAP’s AI Foundation, and co-developing industry-specific solutions for Europe’s most complex industries and administrations. With Helsing, we are accelerating the development of vision-language-action models for real-world defense and security applications.
We are announcing a Series C funding round of 1.7B€ at a 11.7B€ post-money valuation. This investment fuels our scientific research to keep pushing the frontier of AI to tackle the most critical and sophisticated technological challenges faced by strategic industries. The Series C funding round is led by leading semiconductor equipment manufacturer, ASML Holding NV (ASML).
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.
Today we’re introducing Mistral Small 3, a latency-optimized 24B-parameter model released under the Apache 2.0 license.