LFM2.5-VL-3B for Better and Faster Vision Capabilities for the Edge

LFM2.5-VL-3B is our most capable vision-language model you can run on your own hardware. It understands documents and screens alike, grounds objects, and can call tools. It answers directly instead of reasoning, so responses stay fast in real-time and on-device apps.

LFM2.5-VL-3B extends the vision-language capabilities of our previous releases with four major improvements:

- Screen/UI understanding: Strong understanding of digital screens across different devices.

- Grounding: Improved grounding and object detection with natural language queries.

- Multi-image input: Improved reasoning across multiple images.

- Function calling: Significantly stronger at function calling, in text-only and vision-text situations.

How we trained our most capable vision-language model

LFM2.5-VL-3B pairs a SigLIP2 400M NaFlex vision encoder with the same pre-trained backbone as our LFM2.5-2.6B text model. It is pre-trained on about 34T tokens, with 4x more vision data than before, drawn from curated and synthetic image-caption, OCR, grounding, and instruction-following sets. To support non-Latin scripts, we doubled the vocabulary to 128K by extending the tokenizer in place rather than retraining from scratch.

Post-training runs in two stages: First is supervised fine-tuning (SFT), with knowledge distillation from a larger teacher and Antidoom training . Second is multi-reward reinforcement learning (RL).

Benchmark results

We evaluated LFM2.5-VL-3B across both vision and text benchmarks.

The vision benchmarks cover multilingual visual comprehension, instruction following, visual math and scientific reasoning, document understanding, object detection, multi-image understanding, and screen understanding. LFM2.5-VL-3B leads its size class on real-world image tasks, while also reading digital content well, from documents and charts to on-screen UI elements.

Task Benchmark LFM2.5-VL-3B (3.1B) LFM2-VL-3B (3.1B) gemma-4-E2B-it (5.1B) gemma-4-E4B-it (8B) InternVL 3.5 2B (2.4B) InternVL 3.5 4B (4.7B) Qwen3.5-2B (2.3B) Qwen3.5-4B (4.7B)

General MMStar 63.3 57.7 45.3 52.9 57.7 65.5 55.1 59.3

MME 73.1 73.0 54.9 67.6 73.6 81.0 76.2 79.5

RealWorldQA 73.1 71.1 60.0 64.3 61.6 67.7 65.1 67.1

SimpleVQA 35.4 33.0 27.3 30.4 30.5 33.7 35.2 40.7

SEED-Bench (image) 77.7 76.6 71.4 75.3 75.4 76.4 75.8 76.1

MMBench (dev EN v1.1) 81.0 80.0 64.2 71.6 76.2 81.1 73.1 78.4

CountBenchQA 87.3 92.2 70.4 80.5 70.4 82.5 83.8 86.7

Multilingual MMMB 83.0 81.9 73.3 80.4 76.3 81.5 75.9 82.0

Multilingual MMBench 79.5 76.3 62.8 71.2 70.9 76.6 69.9 77.0

Multimodal IF MM-IFEval 60.6 51.4 65.6 68.2 47.1 54.5 55.4 63.1

STEM LogicVista 37.4 32.2 29.5 34.5 30.9 36.2 34.0 37.6