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Qwen3-VL, the most powerful vision language model in the Qwen series is now available on Ollama’s cloud. The models will be made available locally soon. Model capabilities Visual Agent: Operates PC/mobile GUIs—recognizes elements, understands functions, invokes tools, completes tasks; Visual Coding Boost: Generates Draw.io/HTML/CSS/JS from images/videos; Advanced Spatial Perception: Judges object positions, viewpoints, and occlusions; provides stronger 2D grounding and enables 3D grounding for

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ation in one compact system, it enables conversational AI on devices where speed, privacy, and efficiency matter most. With LFM2-Audio, we’re taking another step toward multimodal agents that run privately and in real time on your own devices. Closing LFM2-Audio defines a new class of audio foundation models: lightweight, multimodal, and real-time. By unifying audio understanding and generation in one compact system, it enables conversational AI on devices where speed, privacy, and efficiency ma

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M2; Availability & licensing; Build with Nanos We’re launching Liquid Nanos — a family of 350M–2.6B parameter foundation models that deliver frontier‑model quality on specialized, agentic tasks while running directly on phones, laptops, and embedded devices. In our evaluations with partners, Nanos perform competitively with models hundreds of times larger. The result: planet‑scale AI agents with cloud‑free economics. Why Nanos, and why now? The last few years of AI progress have been powered by

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ent AI visuals for brand and marketing use Google Translate rolls out live conversation translation and adaptive learning exercises Salesforce launches digital twin testing for AI agents in business ops LinkedIn survey shows AI fatigue rising among professionals AI’s Rise to the C-suite: How Algorithms Earned a Seat at the Table Summary: AI is moving beyond routine data tasks and into strategic functions. A recent survey shows 74% of executives trust AI input more than human insight, and 44% are

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Today we're releasing Claude Opus 4.1, an upgrade to Claude Opus 4 on agentic tasks, real-world coding, and reasoning. We plan to release substantially larger improvements to our models in the coming weeks. Opus 4.1 is now available to paid Claude users and in Claude Code. It's also on our API, Amazon Bedrock, and Google Cloud's Vertex AI. Pricing is the same as Opus 4. Claude Opus 4.1 Opus 4.1 advances our state-of-the-art coding performance to 74.5% on SWE-bench Verified. It also improves Clau

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to bring its latest state-of-the-art open weight models to Ollama. The two models, 20B and 120B, bring a whole new local chat experience, and are designed for powerful reasoning, agentic tasks, and versatile developer use cases. Feature highlights Agentic capabilities: Use the models’ native capabilities for function calling, web browsing (Ollama is providing a built-in web search that can be optionally enabled to augment the model with the latest information), python tool calls, and structured

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ve software development. Over the past year, AI coding assistants have introduced powerful capabilities, such as multi-file reasoning, contextual suggestions, and natural-language agents, all directly within the IDE. Despite these improvements, however, adoption inside enterprise environments has been slow. The reasons have less to do with model performance or the interface, and more with how these tools are built, deployed, and governed. Key limitations holding back enterprise teams include: De

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LFM2 delivers 2x faster decode and prefill performance than Qwen3 on CPU. It also significantly outperforms models in each size class, making them ideal for powering efficient AI agents. These performance gains make LFM2 the ideal choice for local and edge use cases. Beyond deployment benefits, our new architecture and training infrastructure deliver a 3x improvement in training efficiency over the previous LFM generation, establishing LFM2 as the most cost-effective path to building capable, g

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ally optimized for low-latency transcription in conversational settings. Available today, Ink-Whisper is the fastest, most affordable STT model–designed for enterprise-grade voice agents. Sonic to Ink: from voice-out to voice-in With Sonic, we’ve become the preferred text-to-speech (TTS) provider for builders who prioritize speed, quality, and reliability. This comes as no surprise, given Sonic’s market leadership in ultra-low latency, enabling customers to create the most realistic interactive

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rks 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 optimized for high-performance code retrieval and semantic understanding. It enables a range of practical applica

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Today, we’re introducing the next generation of Claude models: Claude Opus 4 and Claude Sonnet 4, setting new standards for coding, advanced reasoning, and AI agents. Claude Opus 4 is the world’s best coding model, with sustained performance on complex, long-running tasks and agent workflows. Claude Sonnet 4 is a significant upgrade to Claude Sonnet 3.7, delivering superior coding and reasoning while responding more precisely to your instructions. Alongside the models, we're also announcing: Ext

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ied model, scaling distributed RL to a lot more compute Check out the technical reports for more details: primeintellect.ai/intellect-2 Join Us INTELLECT-2 demonstrates that globally distributed RL works. Now, we're focusing on tool-assisted reasoning, crowdsourcing higher-quality data, and optimizing our infrastructure and training recipe to build frontier open models. Join us if you want to build open source and democratized AGI with us. Rewriting Prime Agent in Rust On the Nature of the Swarm

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orcement Learning. Now that the foundational infrastructure is in place, it’s up to all of us to scale it to the highest-impact domains. Over the coming months, we’ll be: Training agents end-to-end with RL: To fully leverage inference-time compute for scientific and research progress, we need to teach reasoning models to use tools such as code interpreters and other software. Reinforcement learning offers a powerful framework to optimize agent tool use in an end-to-end fashion; Crowdsourcing tas

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for virtual assistants and other applications where quick, accurate responses are essential. Low-latency function calling: Capable of rapid function execution within automated or agentic workflows Fine-tuning for specialized domains: Mistral Small 3.1 can be fine-tuned to specialize in specific domains, creating accurate subject matter experts. This is particularly useful in fields like legal advice, medical diagnostics, and technical support. Foundation for advanced reasoning: We continue to b

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users to extract specific information from documents and format it in structured outputs, such as JSON. Users can chain extracted outputs into downstream function calls and build agents. See this example notebook . For organizations with stringent data privacy requirements, Mistral OCR offers a self-hosting option. This ensures that sensitive or classified information remains secure within your own infrastructure, providing compliance with regulatory and security standards. If you would like to

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n think for. Claude 3.7 Sonnet shows particularly strong improvements in coding and front-end web development. Along with the model, we’re also introducing a command line tool for agentic coding, Claude Code. Claude Code is available as a limited research preview, and enables developers to delegate substantial engineering tasks to Claude directly from their terminal. Claude 3.7 Sonnet is now available on all Claude plans—including Free, Pro, Team, and Enterprise—as well as the Claude Developer P

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pect immediate feedback and near real-time interactions; Low-latency function calling: Mistral Small 3 is able to handle rapid function execution when used as part of automated or agentic workflows; Fine-tuning to create subject matter experts: Mistral Small 3 can be fine-tuned to specialize in specific domains, creating highly accurate subject matter experts. This is particularly useful in fields like legal advice, medical diagnostics, and technical support, where domain-specific knowledge is e

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lance by providing a valuable tool for scientists, and it will also facilitate research in scientific foundation models. However, we emphasize the need for rigorous safety evaluations before open-sourcing larger or more capable genomic models in the future, and are developing a suite of safety benchmarks for this purpose. Team METAGENE-1 is brought to you by a team of researchers from USC, Prime Intellect, and the Nucleic Acid Observatory: Rewriting Prime Agent in Rust On the Nature of the Swarm

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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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ce, and makes it possible to build applications that respect our privacy and rapidly interact with the world at volumes and speeds that seem impossible today. Applications such as agents, personal assistants, robotics, gaming, healthcare, transportation, defense, and security are just a few areas that will transform as this technology emerges. We’ve spent the last few years pioneering a new architecture for AI called state space models (SSMs). Compared to widely used attention architectures, SSM

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