LFM2.5 model releases

Liquid AI's edge vision-language model, released Aug 12 2026 — a 3.1B-parameter VLM built on the LFM2.5-2.6B text base with an integrated SigLIP2 400M NaFlex vision encoder. Accepts text, images, and video frames and returns text, tuned for on-device screen understanding, visual grounding, and tool calling. Vendor-reported: 80.7 average on ScreenSpot-v2 screen understanding, 87.9 P@1 on RefCOCO grounding, 59.5 on ToolSandbox function calling, 81.0 on MMBench, and 69.4 averaged across 28 benchmarks. Runs on-device at ~228 tok/s on an Apple M5 Max and ~116 tok/s on an AMD Ryzen AI Max+ 395; supported in llama.cpp, MLX, vLLM, SGLang, and ONNX. Open weights on Hugging Face.

Liquid AI's on-device agentic model, released Aug 6 2026 (surfaced on hosted platforms ~Aug 11) — a 2.69B-parameter dense model, distinct from the LFM2.5-8B-A1B MoE. Uses Liquid's hybrid stack across 30 layers: 22 double-gated short-convolution blocks plus 8 grouped-query-attention blocks, with a 128K-token vocabulary and a 131,072-token context, pre-trained on ~34T tokens across 16 languages. Text-only, built to plan, call tools, and complete multi-step tasks entirely on-device (phone, laptop, PC, or robot) so data never leaves the device and the marginal cost per run is near zero; Liquid reports tool-use and instruction-following competitive with models ~4x its size (e.g. leading Qwen3.5-9B on ToolSandbox, Multi-IF, and IFStruct), while explicitly not recommending it for agentic coding or knowledge-heavy work. Decodes at ~220 tok/s on an Apple M5 Max in under 2.5GB and ~30 tok/s on a phone. Open weights (base + post-trained) on Hugging Face under the LFM Open License (lfm1.0), shipped day-one in GGUF, MLX, and ONNX.

Liquid AI's on-device Mixture-of-Experts model: 8.3B total parameters with only ~1.5B active per forward pass (32 experts, 4 active per token). Uses Liquid's hybrid architecture — 18 double-gated LIV convolution blocks plus 6 grouped-query-attention layers — for a 131K-token context that runs in under ~6GB of memory on consumer hardware. A reasoning-only model that emits an explicit chain of thought before its answer, with strong tool-calling and agentic performance for its size. Builds on the October 2025 LFM2-8B-A1B, expanding the context window to 128K and scaling pretraining from 12T to 38T tokens. Released May 28 2026 under the LFM Open License; caught in a July catalog-gap sweep.