Liquid AI Releases LFM2.5-VL-3B: A 3B Vision-Language Model That Reads Screens, Grounds Objects, and Calls Tools On-Device

Liquid AI released LFM2.5-VL-3B, a 3.1B-parameter vision-language model for on-device deployment, averaging 69.4 across 28 vision benchmarks, matching InternVL-3.5-4B and 0.7 points behind Qwen3.5-4B. The model reads screens, grounds objects, and calls tools, fitting in ~3 GB and decoding 228 tokens/s on Apple M5 Max. It ships in native, GGUF, ONNX, and MLX formats, with free commercial use for companies under $10M annual revenue.

Yesterday, Liquid AI https://www.liquid.ai/ released LFM2.5-VL-3B https://www.liquid.ai/blog/lfm2-5-vl-3b . It is a 3.1B-parameter vision-language model built for on-device deployment. The model reads digital screens across mobile, web, and desktop. It grounds objects to coordinates, parses documents and charts, and calls tools from text or image input. Liquid AI reports an average of 69.4 across 28 vision benchmarks. That matches InternVL-3.5-4B and sits 0.7 points behind Qwen3.5-4B, both 4.7B models. The model is non-reasoning, so it answers directly and keeps latency low. It fits in roughly 3 GB of memory and decodes 228 tokens/s on an Apple M5 Max. Is it deployable? Yes, the checkpoint ships in four formats: native https://huggingface.co/LiquidAI/LFM2.5-VL-3B , GGUF https://huggingface.co/LiquidAI/LFM2.5-VL-3B-GGUF , ONNX https://huggingface.co/LiquidAI/LFM2.5-VL-3B-ONNX , and MLX https://huggingface.co/LiquidAI/LFM2.5-VL-3B-MLX-8bit . Day-one runtimes include llama.cpp, MLX, vLLM, SGLang, and ONNX. It fits in roughly 3 GB of memory. Which company levels : The LFM Open License v1.0 https://www.liquid.ai/lfm-license is Apache-2.0-based with one change: free commercial use ends once a company’s annual revenue reaches $10M USD. So indie developers, startups, and SMBs under that line can ship commercially at no cost. Enterprises above it must negotiate a commercial license with Liquid AI. Research, education, and non-profit use carry no revenue limit. Industries : Consumer electronics, automotive https://www.liquid.ai/automotive , industrial and robotics https://www.liquid.ai/industrial-robotics , financial services https://www.liquid.ai/finance , healthcare https://www.liquid.ai/healthcare , and e-commerce https://www.liquid.ai/e-commerce . Also QA and RPA vendors that automate GUIs. Applications : On-device screen agents, GUI test automation, PDF-to-structured-text with layout labels, invoice and receipt OCR, near-real-time object detection in vehicles, offline translation of menus and road signs, and multi-image comparison. So, What is new? LFM2.5-VL-3B extends LFM2-VL-3B https://www.liquid.ai/blog/lfm2-vl-3b-a-new-efficient-vision-language-for-the-edge along four axes. Screen and UI understanding : The model averages 80.7 on ScreenSpot-v2 across desktop 78.7 , mobile 81.2 , and web 82.2 . Liquid AI reports Gemma-4-E4B at 51.2 and Qwen3.5-4B at 78.5, with the larger InternVL-3.5-4B ahead at 84.1. Function calling : This is new to the VL line. ToolSandbox moves from 26.4 to 59.5. BFCL v4 moves from 20.5 to 32.5. Tool calls are emitted as Pythonic calls between <|tool call start| and <|tool call end| tokens. Grounding : RefCOCO-avg precision@1 rises from 57.1 to 87.9, a 30-point gain driven by scaled synthetic grounding data. Multi-image input : BLINK improves from 50.2 to 61.5, and MuirBench from 34.9 to 58.3. Architecture and training The language backbone is LFM2.5-2.6B https://www.liquid.ai/blog/lfm2-5-2-6b . The vision tower is a SigLIP2 NaFlex shape-optimized 400M encoder. NaFlex handles native resolution by splitting large images into non-overlapping 512×512 patches plus a resized whole-image thumbnail. Context length is 32,768 tokens, and 16 languages are supported. Pre-training used approximately 34T tokens. Vocabulary was doubled to 128K by extending the existing tokenizer in place https://www.liquid.ai/blog/tokenizer-expansion , which improves non-Latin script coverage. Vision pre-training was scaled 4× in tokens with curated and synthetic caption, OCR, grounding, and instruction-following data. Post-training is SFT with knowledge distillation from a larger teacher and Antidoom training https://www.liquid.ai/blog/antidoom , followed by multi-reward reinforcement learning. The model is non-reasoning. It answers directly, which is the design choice behind its latency profile. Benchmarks Liquid AI evaluated across 28 vision benchmarks using vLLM 0.26.0 in non-reasoning mode. LFM2.5-VL-3B averages 69.4, matching InternVL-3.5-4B 69.4 and landing 0.7 points behind Qwen3.5-4B 70.1 . Both comparison models are 4.7B parameters. Notable individual results: RealWorldQA 73.1 against InternVL-3.5-4B at 67.7, TextVQA 84.3 against Qwen3.5-4B at 81.2, MMStar 63.3, MathVista-mini 68.5, ChartQA 81.3, DocVQA 91.1, and OCRBench v1 84.2. CountBenchQA regressed to 87.3 from 92.2 in the prior release. On text-only evaluation, IFEval reaches 82.3, up from 72.9. Gemma-4-E4B still leads there at 87.9. Key Takeaways - LFM2.5-VL-3B hits a 69.4 average across 28 vision benchmarks, matching 4.7B-class models. - ScreenSpot-v2 jumps to 80.7 and RefCOCO-avg to 87.9, from 57.1 in the prior release. - Function calling is new to the VL line: ToolSandbox 26.4 → 59.5, BFCL v4 20.5 → 32.5. - Runs in ~3 GB, decoding 228 tok/s on M5 Max and 20 tok/s on a Galaxy S26 Ultra. - LFM Open License v1.0 is free commercially only under $10M annual revenue. Check out the Technical Details and . Model Weights https://huggingface.co/LiquidAI/LFM2.5-VL-3B Also, feel free to follow us on and don’t forget to join our Twitter https://x.com/intent/follow?screen name=marktechpost and Subscribe to 150k+ML SubReddit https://www.reddit.com/r/machinelearningnews/ . 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