Today we release 𝚍𝟷-𝚞𝚕𝚝𝚛𝚊, our most powerful decision model. > built for hard decision problems. > d1-ultra is multimodal > supports 44 languages > #1 on the @ArtificialAnlys Decision Index (65) > leads all eight categories of our 112-evaluation suite 1/

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. 1/

Today, we release DSpark draft models for LFM2.5-1.2B-Instruct, LFM2.5-2.6B, and LFM2.5-8B-A1B. These add a speculative decoding path that trades a minimal memory increase for a large decoding speedup without changing output quality. A lightweight draft model proposes a block of candidate tokens and the target model verifies them in a single forward pass. Across MATH500, GSM8K, HumanEval, MBPP, and MT-Bench at batch size 1: > Up to 3.18x throughput on an H100: LFM2.5-8B-A1B on MATH500, 428 → 1362 tok/s > Up to 2.87x on an M4 Max MacBook Pro: LFM2.5-1.2B-Instruct on HumanEval, 136 → 389 tok/s > LFM2.5-2.6B means: 2.67x on the H100 (323 → 864 tok/s), 2.27x on device (61 → 139 tok/s) > Under greedy decoding, the emitted sequence is identical to baseline by construction, so benchmark accuracy is unchanged. Each draft model is around 300M parameters, with embedding and LM head tied to its target model. The gain shows most in agentic workloads, where the model reasons before every tool call and the user waits through it all: on BFCL multi-tool scenarios, DSpark cuts LFM2.5-2.6B latency by nearly 50% on average. 🧵

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 🧵

Introducing LFM2.5-VL-1.6B-Extract and LFM2.5-VL-450M-Extract: Vision-language models that return structured JSON, not free-form text. Pass in an image and a list of fields. Get back a clean JSON object. > Two sizes: 1.6B parameters and 450M > open-weight > run on any device SoC 🧵