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.
Introducing π»π΅πΌπΉ: 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. > π»π΅πΌπΉ-π·.π½π±: text, 256k context window. > π»π΅πΌπΉ-π π»-πΈπ±: vision-language model with video-input support for spatial understanding > π»π΅πΌπΉ-π°ππππ-πΈπ±: tool calls, voice chat, reasoning in 18 languages. > π»π΅πΌπΉ-πΎπππ-πΈπ±: an omni model combines vision, audio, and language. > ππ·-πΈπ± and ππ·-πΎπππ-πΈπ±: 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. > π»π΅πΌπΉ-π π»-πΈπ± 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. > π»π΅πΌπΉ-π·.π½π± is #1 on the Pareto frontier of device intelligence πππππππ.ππππππ.ππ Every modality for specialized intelligence. Pick the one that fits your use case, customize it, and run it on-device. 1/ π§΅
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Original title: Liquid AI just launched LFM3, an open-weight model family whose 1.7B text model beats its 2.6B predecessor.
