Liquid AI, created by former MIT computer scientists, has introduced its smallest AI language model yet, LFM2.5-230M. This 230-million-parameter model is engineered for on-device agentic workflows, enabling it to run efficiently on smartphones, laptops, and robotics. Despite its small size, it surpasses larger models—more than four times its size—in data extraction tasks, outperforming Alibaba’s 800M parameter Qwen3.5-0.8B and Google’s 1B parameter Gemma 3 1B.
LFM2.5-230M focuses on developers and engineers building lightweight data extraction pipelines and edge systems. It operates under a dual-use commercial license, free for entities earning below $10 million annually, while larger enterprises require a paid license. The model’s unique LFM2 architecture combines gated short-range convolutions with grouped-query attention, allowing high inference speed and a memory footprint below 400MB. It supports a large 32K context window for processing long documents or data streams.
Performance benchmarks show the model’s efficiency, decoding 213 tokens per second on a Samsung Galaxy S25 Ultra and 42 tokens per second on a Raspberry Pi 5. It excels specifically in AI ETL tasks, offering a cost-effective alternative to expensive cloud-based models by running locally. LFM2.5-230M scores notably well on benchmarks like BFCLv3 and CaseReportBench, demonstrating superior tool-use and data extraction performance compared to larger models.
The model also serves advanced research needs, demonstrated by its deployment on a Unitree G1 humanoid robot for on-device command processing and multi-step planning. The base and post-trained models are available on Hugging Face, with broad support for popular inference platforms.
Liquid AI released LFM2.5-230M under a dual-use license that is free for smaller users but restricts commercial use by companies earning over $10 million annually, requiring paid agreements for larger enterprises. This approach balances open accessibility with commercial protection.
