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samples out of 1,403 total samples in order to ensure the benchmarking is performed only on English documents.olmOCR-Bench-English. The implementation details can be found in this github repository. olmOCR (Category-wise Performance Comparison) OmniDocBench V1.5 A comprehensive benchmark for evaluating document parsing, featuring various document and layout types (academic papers, financial reports, and handwritten notes). We report the performance on the official English-only split from the eva

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following the best practices defined for Mistral Vibe CLI . We’re excited to see what you will build with Devstral 2, Devstral Small 2, and Vibe CLI! Share your projects, questions, or discoveries with us on X/Twitter , Discord , or GitHub . If you’re interested in shaping open-source research and building world-class interfaces that bring truly open, frontier AI to users, we welcome you to apply to join our team . Devstral 2 Open-weights agentic coding model for autonomous software engineering.

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Need a tailored solution? Contact our team to explore fine-tuning or enterprise-grade training. Share your projects, questions, or breakthroughs with us: Twitter/X , Discord , or GitHub . Alongside this launch, you can explore the full details of the Ministral 3 series architecture in our latest research paper here . We believe that the future of AI should be built on transparency, accessibility, and collective progress. With this release, we invite the world to explore, build, and innovate wit

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g, and evaluation. Technical Report: storage.googleapis.com/intellect-3-paper/INTELLECT 3 Technical Report.pdf; Model Weights: huggingface.co/PrimeIntellect/INTELLECT-3; PRIME-RL: github.com/PrimeIntellect-ai/prime-rl; Verifiers: github.com/PrimeIntellect-ai/verifiers; Environments: hub.primeintellect.ai Future Work We’re looking forward to extending this work to: Scaling Agentic RL: For the INTELLECT-3 checkpoint we release, rewards and evaluations continue to rise, and training remains stable.

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s the best frontier task planning and tool calling we’ve seen yet.Jeff Wang CEO Claude Opus 4.5 delivers high-quality code and excels at powering heavy-duty agentic workflows with GitHub Copilot. Early testing shows it surpasses internal coding benchmarks while cutting token usage in half, and is especially well-suited for tasks like code migration and code refactoring.Mario Rodriguez Chief Product Officer Claude Opus 4.5 beats Sonnet 4.5 and competition on our internal benchmarks, using fewer t

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accessible, high-quality, open source safety tools for digital organizations of all kinds in the age of AI. Established in 2025 by a diverse group of leading technology companies, philanthropic organizations and academic institutions, ROOST believes that solutions for online safety can best be achieved by providing organizations with innovative open source tools and technical support. Reference OpenAI blog; OpenAI gpt-oss-safeguard developer cookbook; ROOST’s model community repository on GitHub

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lysis of standard data, we also conducted 1-on-1 comparisons of practical performance against Claude 4.5 Sonnet and several open-source models: These test sets will be released on GitHub this week. To make it easier for everyone to use Agent-related capabilities, we have launched our M2-powered Agent product in China and upgraded the overseas version. In MiniMax Agent, we offer two modes: Lightning Mode: A high-efficiency, high-speed Agent for instant output in scenarios like conversational Q&A,

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our premium tier model—that's a game-changer for our unit economics.Jon Noronha Co-Founder, Gamma Our early testing shows that Claude Haiku 4.5 brings efficient code generation to GitHub Copilot with comparable quality to Sonnet 4 but at faster speed. Already we're seeing it as an excellent choice for Copilot users who value speed and responsiveness in their AI-powered development workflows.Matthew Isabel Distinguished Product Manager Safety evaluations We ran a detailed series of safety and ali

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ript Library Install Ollama’s JavaScript library Pull the model Example non-streaming output with image Example streaming the output with image Ollama’s JavaScript library page on GitHub has more examples and API documentation. Python Library Install Ollama’s Python library Pull the model Example non-streaming output with image Example streaming the output with image Ollama’s Python library page on GitHub has more examples and API documentation. API The model can also be accessed directly on oll

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ements on longer horizon tasks. It reinforces why many developers using Cursor choose Claude for solving their most complex problems.Michael Truell CEO Claude Sonnet 4.5 amplifies GitHub Copilot's core strengths. Our initial evals show significant improvements in multi-step reasoning and code comprehension—enabling Copilot's agentic experiences to handle complex, codebase-spanning tasks better.Mario Rodriguez Chief Product Officer Claude Sonnet 4.5 is excellent at software development tasks, lea

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fr, and Hellaswag-fr while preserving competitive English performance. All training code, evaluation scripts, and the Luth-SFT dataset (338 million tokens) are openly available on GitHub to support further French NLP research. Availability & licensing Liquid Nanos are available today on the Liquid Edge AI platform (LEAP) for iOS, Android, and laptops. Developers can use them out of the box, integrate via SDKs, or compose them into agents. Models are also available on Hugging Face. We’re making t

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copy patterns from other services. Before changing the retry logic, they need to understand how similar failures are handled elsewhere. Instead of switching to Slack or searching GitHub manually, they enter a query directly in the IDE: “How do we handle Stripe timeouts in the checkout flow?” The embedding index, running locally, returns a helper module from another service that wraps retry logic with exponential backoff. They copy the pattern into their own handler—but realize three other servi

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es, document styles, and content formats. For this evaluation, we used a curated dataset of articles covering a range of real-world formats and content types. The dataset included GitHub Markdown files, scanned PDFs that were digitized into Markdown using Vision-Language Models (VLMs), documents containing mathematical equations written in LaTeX, and chemistry documents featuring complex chemical notations. It also incorporated code files with embedded comments and documentation, as well as web

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tegories. The details of the benchmarks corresponding to each category can be found in the table in the “Benchmarks details” section. SWE-Bench is based on a dataset of real-world GitHub issues and corresponding fixes, and is especially relevant for retrieval-augmented generation for coding agents. Text2Code (GitHub) contains benchmarks relevant for giving context for code completion or edition. We believe that these two categories are especially relevant to code assistants. Codestral Embed is o

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ying relationships between disparate components, and identifying subtle bugs in intricate functions. Devstral is designed to tackle this problem. Devstral is trained to solve real GitHub issues; it runs over code agent scaffolds such as OpenHands or SWE-Agent, which define the interface between the model and the test cases. Here, we show Devstral’s performance on the popular SWE-Bench Verified benchmark, a dataset of 500 real-world GitHub issues which have been manually screened for correctness.

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ng a 7B parameter metagenomic foundation model designed for pandemic monitoring, trained on over 1.5T base pairs of DNA and RNA sequenced from wastewater. Paper: metagene-1-paper; Github: github.com/metagene-ai; Hugging Face: huggingface.co/metagene-ai A collaboration between researchers at USC, Prime Intellect, and the Nucleic Acid Observatory. Overview We pretrain METAGENE-1, a 7-billion-parameter autoregressive transformer language model, which we refer to as a metagenomic foundation model, o

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p toward democratizing AI development and preventing the consolidation of AI capabilities within a few organizations. By open-sourcing INTELLECT-1's model, checkpoints, and training framework, we invite the global AI community to join us in pushing the boundaries of distributed training. To get involved, visit our GitHub repository or join our Discord community. Together, we can build a more open, collaborative future for AI development. ‍ Rewriting Prime Agent in Rust On the Nature of the Swarm

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