Liquid AI on Oct. 27 released LFM3, a family of six open-weight models in the 2-billion-parameter class that handle text, vision, audio, combined "omni" inputs and decision tasks, all built on a shared LFM3-1.7B-Base backbone.
The release is aimed at teams that want AI running on their own hardware. Liquid said in its launch blog that every model can be fine-tuned on a single GPU. The benchmarks behind the launch are company-reported, and VentureBeat found no independent evaluations as of Oct. 29.
What's in the family
Liquid's model catalog lists LFM3-1.7B for text, LFM3-VL-2B for vision and video, LFM3-Audio-2B for speech in and out, and LFM3-Omni-2B, which takes in text, images, video and audio and responds in text and speech. Two decision models, d1-2B and d1-Omni-2B, round out the set.
According to its Hugging Face model card, the VL model is the first in Liquid's LFM line to accept video. The Audio model handles 18 languages and relies on a tokenizer the company calls LiquidCodec. The Omni model produces 24 kHz speech and has a 32K context window, meaning it can consider roughly 32,000 tokens of input at a time.
The decision models sit on top of the others: d1-2B is built on LFM3-VL-2B, and d1-Omni-2B on LFM3-Omni-2B. Liquid said a single API call returns calibrated probabilities across a fixed set of outcomes, and that the models can "answer many questions in a single forward pass."
Size, speed and benchmarks
The text model has 1.73 billion parameters and a 256K context window. "The text model fits in about 1 GB at 4-bit precision," the Liquid AI team wrote in its Hugging Face launch blog. That is small enough for a phone.
On Liquid's own 29-benchmark average, LFM3-1.7B scored 49.37. Qwen3.5-9B came in at 47.24, Qwen3.5-4B at 39.27, MiniCPM5-2B at 39.19, Gemma4 E4B at 31.74 and Liquid's earlier LFM2.5-2.6B at 31.55. The company said LFM3-1.7B beats that predecessor with 35% fewer parameters.
Agentic tasks, where a model carries out multi-step work such as navigating websites, drove much of the gap. VentureBeat reported that on WebArena-Verified, LFM3-1.7B scored 38.42 against 28.94 for Qwen3.5-9B and 3.02 for LFM2.5-2.6B. On AutomationBench the figures were 41.29, 15.10 and 0.94.
Bigger models still hold the edge in other areas. On GPQA Diamond, a graduate-level science test, LFM3-1.7B scored 63.37 to Qwen3.5-9B's 80.40. On RULER 128K, which probes long-context recall, the split was 65.56 to 87.11, and on SWE-bench Verified, a coding benchmark, it was 43.40 to 51.20. On The Wire reported that Qwen3.5-9B leads on 15 of the 29 individual tests.
