Liquid AI

Liquid AI released LFM2.5-24B-A2B, an open-weight reasoning mixture-of-experts model with 24B total and 2B active parameters and a 131K-token context window. The company claims it is competitive with Qwen3.6-35B-A3B on agentic and tool-use tasks while decoding at 184 tokens/s on an Apple M5 Max.

Liquid AI released two open-weight decision models, d1-3B (text and image) and the experimental d1-omni-600M (text with image or audio). Unlike generative models, they answer in a single forward pass, and Liquid AI claims d1-3B scores 48.57 on its Decision Index 0.2.1, ahead of all 4B and 9B models it tested.

Liquid AI has released LFM2.5-VL-3B-DSpark, a 280M-parameter speculative decoding drafter for its LFM2.5-VL-3B vision-language model. The company claims decode speedups up to 3.13x on Apple silicon and 2.66x on H100 GPUs, with day-one support for llama.cpp, MLX-VLM, and SGLang.

Liquid AI released DSpark draft model checkpoints for three LFM2.5 models, enabling speculative decoding that speeds up inference by up to 3.18x on H100 GPUs and 2.87x on-device, with day-one support for llama.cpp and SGLang.

Liquid AI has released LFM2.5-VL-3B, a 3.1-billion-parameter vision-language model built for on-device deployment. The model claims leading performance in its size class on grounding, screen understanding, and tool use, while running at 228 tokens/s on an Apple M5 Max.

Liquid AI has released LFM2.5-2.6B, a 2.6B-parameter model designed to run capable tool-calling agents locally on laptops and phones. The company claims it matches or beats models up to 4x its size on instruction-following and tool-use benchmarks while running under 2.5GB of memory.

Liquid AI released two open-weight encoder models, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, built for classification and routing tasks at long context on CPU hardware. The company claims the smaller model runs 3.7x faster than ModernBERT-base at 8,192 tokens while matching or beating larger encoders on GLUE, SuperGLUE, and multilingual benchmarks.

Liquid AI has released LFM2.5-230M, a 230M parameter hybrid model trained on 19 trillion tokens with a 32,768 token context window. The model achieves 213 tok/s decode speed on Galaxy S25 Ultra and 42 tok/s on Raspberry Pi 5, with support for function calling and data extraction tasks.

Liquid AI has released LFM2.5-8B-A1B, an 8-billion parameter hybrid model designed specifically for edge AI and on-device deployment. The model is available in multiple GGUF quantized formats ranging from 4-bit (4.84 GB) to 16-bit (16.9 GB), optimized for memory efficiency.