Sina Semnani

Today we release Open d1: two open-weight multimodal models in our d1 decision model family.

> d1-3B: text + vision
> d1-omni-600M: text + image or text + audio
> Real-time decision making anywhere, from data centers such as @nvidia DGX to RTX workstations to Jetson at the edge.

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Today, we release an experimental DSpark draft model for LFM2.5-VL-3B, bringing speculative decoding to our vision-language models.

A lightweight drafter proposes multiple tokens ahead, and the target model verifies them together in a single pass. This accelerates generation without changing output quality.

Across six vision-language task categories at batch size 1 and temperature 0:

> MLX on M5 Max: up to 3.13x faster decoding and 2.62x end to end
> llama.cpp on M3 Ultra: up to 2.14x faster decoding and 1.77x end to end
> SGLang on H100: up to 2.66x faster decoding and 2.27x end to end

All evaluations were collected using Pipette, the benchmarking infrastructure behind Liquid AI's public device-performance data.

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Today, we release LFM2.5-VL-3B, a lightweight vision-language model that reads screens, documents, and the physical world. It handles digital screens across mobile, web, and desktop, grounds objects to coordinates, reads text and charts, and calls tools from either text or image input.

Built on LFM2.5-2.6B base, with a SigLIP2 400M NaFlex vision encoder

> Pre-trained on ~34T tokens
> Vocab size: 128K
Comparable or better scores compared to models up to 2.6x its size:
> ScreenSpot-v2 80.7, ahead of Gemma-4-E4B at 51.2
> RealWorldQA 73.1, ahead of InternVL-3.5-4B at 67.7
> TextVQA 84.3, ahead of Qwen3.5-4B at 81.2
> RefCOCO-avg 87.9, up from 57.1 on LFM2-VL-3B
> ToolSandbox 59.5, up from 26.4 on LFM2-VL-3B

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