DeepSeek Releases V4-Flash-0731, Outperforming Its Bigger Model
DeepSeek's official V4-Flash-0731 release outperforms its larger V4-Pro model on coding and agent benchmarks, despite activating far fewer parameters per token.
Read more →Every AI News story tagged with both Open-Source AI and Open-Source AI Models — the two topics side by side, updated as new articles publish.
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DeepSeek's official V4-Flash-0731 release outperforms its larger V4-Pro model on coding and agent benchmarks, despite activating far fewer parameters per token.
Read more →LG AI Research released K-EXAONE 2.0, a 750-billion-parameter open-weight model under the Apache 2.0 license, aiming to rival Chinese and US frontier AI labs.
Read more →Ant Group's Ling-3.0-Flash matches models several times its size on reasoning and coding benchmarks using just 5.1 billion active parameters, the company says.
Read more →Beijing-based Moonshot AI has published the full open weights for Kimi K3, a 2.8-trillion-parameter model it calls the largest open model released to date.
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Poolside is a startup that builds AI models trained specifically to write and fix code, then gives the weights away for free. Here's who is behind it and how the models work.
Read more →Poolside's new 118-billion-parameter Laguna S 2.1 matches or beats far larger rivals on agentic coding benchmarks and ships under an open license.
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Mistral AI is the Paris-based lab racing to build Europe's answer to OpenAI and Anthropic — known for releasing open-weight models and for its 'sovereign AI' pitch to European governments.
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Kimi K3 is a giant open-weight AI model from China's Moonshot AI. Here's what it is, who built it, and why its release rattled Nvidia and chip investors.
Read more →China's Moonshot AI released Kimi K3, a 2.8-trillion-parameter open model with a 1-million-token context window — a system the company calls the world's first open 3-trillion-class AI model.
Read more →PrismML has released Bonsai 27B, a 27-billion-parameter open-weight model compressed to under 4GB, small enough to run natively on a phone while keeping most of its reasoning ability.
Read more →Mira Murati's startup released Inkling, a 975-billion-parameter open-weight model built for developers to fine-tune and adapt, rather than to top leaderboards.
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DeepSeek is the Chinese AI lab whose cheaply trained, open-weight models triggered Nvidia's biggest one-day stock drop and reshaped how the industry talks about AI costs.
Read more →Meta now builds two separate AI model families: the open-weight Llama and the new proprietary Muse. Here's how they differ, and how to try either one.
Read more →GLM is the open-weight AI model family from Chinese lab Zhipu AI (Z.ai) — free to download and self-host, and competitive with top US models at a fraction of the API cost.
Read more →Z.ai has released GLM-5.2, a 744-billion-parameter open-weight model that matches GPT-5.5 on key benchmarks and trails Claude Opus 4.8 narrowly — trained entirely on Huawei silicon with no Nvidia hardware.
Read more →Mistral's new open-source model for Lean 4 fully saturates the miniF2F theorem benchmark, solves 87% of graduate-level math tests, and found five previously unknown bugs in production code — at roughly $4 per proof versus $300 for competing systems.
Read more →Chinese technology company Meituan has released LongCat-2.0, a 1.6-trillion-parameter coding model trained entirely on domestic Chinese chips, under the MIT License — the first frontier-class open-source model built without Western semiconductors.
Read more →Open-weight AI models release their trained parameters publicly so anyone can download, run, or fine-tune them — unlike closed models such as GPT-4 or Gemini, which are accessible only through paid APIs. Here is what that difference means in practice.
Read more →Qwen (Tongyi Qianwen) is Alibaba Cloud's family of open-weight AI models — covering language, code, vision, audio, and more — and the world's most-downloaded AI model series as of 2026.
Read more →Open-source AI models release their trained weights publicly, letting anyone run, study, or customize them without paying API fees. This guide explains what that means in practice and how to get started.
Read more →Liquid AI released LFM2.5-230M, a 230-million-parameter open-weight model that runs AI agent tasks at 213 tokens per second on a phone CPU and 42 tokens per second on a Raspberry Pi — without a cloud connection.
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