Liquid AI Launches Open-Weight LFM3 Family with Tri-Hybrid Architecture
Liquid AI has released the open-weight LFM3 model family, featuring a tri-hybrid architecture that combines short convolutions, sliding-window attention, and global attention. The 1.7B text model outperforms its 2.6B predecessor and competitors like Qwen3.5-9B on agent benchmarks, offering six specialized instances for text, vision, audio, and omni-modal tasks optimized for on-device deployment.
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Rohan Paul (@rohanpaul_ai) · 2026-10-27 17
Liquid AI just launched LFM3, an open-weight model family whose 1.7B text model beats its 2.6B predecessor.
The gain comes from a tri-hybrid design: of 24 layers, 13 are short convolutions, 5 use 256-token sliding-window attention and 6 attend globally.
That efficiency carries into agent benchmarks run by Liquid, where the 1.7B model scores 41.29 on AutomationBench while Qwen3.5-9B manages 15.10.
↳ 引用 @liquidai (Liquid AI): Introducing 𝙻𝙵𝙼𝟹: our new generation of Liquid Foundation Models. With a new tri-hybrid architecture and new multimodal encoders, the LFM3 family is the ideal model suite for specialized intelligence.
6 model instances, across text, audio, vision, and omni modalities, generative and decision models. 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱: text, 256k context window. 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱: vision-language model with video-input support for spatial understanding 𝙻𝙵𝙼𝟹-𝙰𝚞𝚍𝚒𝚘-𝟸𝙱: tool calls, voice chat, reasoning in 18 languages. 𝙻𝙵𝙼𝟹-𝙾𝚖𝚗𝚒-𝟸𝙱: an omni model combines vision, audio, and language. 𝚍𝟷-𝟸𝙱 and 𝚍𝟷-𝙾𝚖𝚗𝚒-𝟸𝙱: decision models with typed outputs and calibrated probabilities in a single pass. Omni adds audio input. fine-tune on 1 GPU. a new tri-hybrid architecture architecture optimized for 14 processor classes across GPUs, CPUs, and NPUs. 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱 is a beast. It performs competitively with Gemma 4 12B (6x larger), and Gemini 3.5 Flash Lite, and GPT-5.6 Luna, with 1.9B parameters. 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱 is #1 on the Pareto frontier of device intelligence 𝚙𝚒𝚙𝚎𝚝𝚝𝚎.𝚕𝚒𝚚𝚞𝚒𝚍.𝚊𝚒
Every modality for specialized intelligence. Pick the one that fits your use case, customize it, and run it on-device.
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