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FASTVLM VS LFM2.5-VL
LFM2.5-VL is Liquid AI’s 2026 edge vision family: a 450M model released in April and a 3B model in August that adds grounding, screen understanding and function calling. FastVLM is Apple’s 2025 family built around encoder efficiency. Here is what each documents.
| Feature | FastVLM | LFM2.5-VL |
|---|---|---|
| Developer | Apple researchers | Liquid AI |
| Sizes and release | 0.5B · 1.5B · 7B (2025) | 450M (8 Apr 2026) · 3B = 2.6B LM + SigLIP2 400M NaFlex (12 Aug 2026) |
| Object grounding (boxes) | Not a documented feature | Yes — RefCOCO-avg 87.9 (3B), 81.28 RefCOCO-M (450M), Liquid-reported |
| Screen / UI understanding | Strong document/OCR scores; no ScreenSpot figures published | ScreenSpot-v2 78.7 desktop · 81.2 mobile · 82.2 web (3B, Liquid-reported) |
| Function / tool calling | Not a documented feature | Yes |
| Multi-image | Single image in official examples | Yes |
| Browser (WebGPU) | 0.5B ONNX (this site) | LFM2.5-VL-3B-ONNX and an official WebGPU Space |
| Runtimes | Transformers · MLX (official app) · Core ML · Transformers.js | llama.cpp · MLX · vLLM · SGLang · ONNX · LEAP (day-one, Liquid-stated) |
| Reported on-device speed | Up to 85× faster TTFT vs LLaVA-OneVision-0.5B at 1152 px; no tokens/s published | 3B: 228 tok/s on M5 Max, 20 tok/s on Galaxy S26 Ultra, ≈3 GB memory (Liquid) |
| License | Apple ML Research license | LFM Open License (check the model card for the current version) |
Checked 2026-09-17
Decision summary
- LFM2.5-VL documents more capabilities—boxes, tool calls, multi-image, GGUF—so it is the easier fit for agent-style edge apps.
- FastVLM remains the reference for encoder efficiency at high resolution and has the tightest Apple-platform integration (MLX app, Core ML assets).
- Both have browser builds; the 3B LFM model needs roughly three times the memory of FastVLM-0.5B.
Choose FastVLM when
- You want the smallest high-resolution model for a browser or iPhone demo
- Your pipeline is MLX or Core ML on Apple hardware
- Latency to first token on large images is the metric you optimise
Choose LFM2.5-VL when
- You need bounding boxes, UI grounding or function calling
- You ship with llama.cpp/GGUF across Android, Windows and Linux
- You want Liquid’s published throughput numbers as a starting point
How to read this table
Every number above is vendor-reported on the vendor’s own hardware and evaluation harness. FastVLM’s 85× figure is a TTFT ratio at one resolution; Liquid’s tokens/s are decode rates on specific devices. Neither is a general ranking.
Questions
Does FastVLM support bounding boxes?
Apple’s model cards do not document grounding outputs. You can ask it to describe positions in text, but there is no evaluated box format as in LFM2.5-VL.
Which runs on Android?
LFM2.5-VL ships GGUF for llama.cpp and Liquid publishes phone numbers. FastVLM has no official Android path; community GGUF or ONNX ports may exist but are not covered here.
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