Liquid AI releases open d1-3B decision model: 16 ms on Jetson AGX Thor, 48.57 on Decision Index

Liquid AI released two open-weight decision models on October 7, 2026: d1-3B, which takes text and images, and d1-omni-600M, an experimental model that takes text plus either an image or audio. Liquid AI claims d1-3B scores 48.57 on its own Decision Index 0.2.1, the best result under 10B parameters, and that it answers a single question in 16 ms on an NVIDIA Jetson AGX Thor.

What a decision model is

Decision models do not generate tokens. They answer structured questions (yes/no, multiple choice, scored criteria) in a single forward pass. Both models are built on Liquid AI's Liquid Foundation Models (LFMs), but from different backbones:

• d1-3B is trained from LFM2.5-VL-3B, a decoder-only vision-language model. Inputs: text and images.
• d1-omni-600M is trained from LFM2.5-Encoder-350M, a bidirectional encoder, with added vision and audio encoders. Inputs: text and image, or text and audio. Liquid AI labels it an early research release.

Benchmarks (company-reported)

On the Decision Index 0.2.1, Liquid AI says d1-3B scores 48.57, ahead of every 4B and 9B model it tested and of Decider 35B-A3B (47.11). The company ran the comparisons itself, and the index is its own benchmark.

On seven public datasets, d1-3B has a mean of 82.9 and d1-omni-600M has 78.4. The comparison models are Decider 2B (77.1) and Decider 4B (81.1).

Benchmark	d1-omni-600M	d1-3B	Decider 2B	Decider 4B
SQuAD 2.0	74.0	83.3	67.7	76.0
Civil Comments	95.8	93.3	93.6	92.8
MASSIVE intent	86.1	86.9	81.1	88.3
PubMedQA	61.3	68.3	65.7	63.3
BoolQ	77.7	86.3	87.3	89.0
XNLI	74.7	85.6	85.0	88.6
PAWS-X	79.5	76.4	59.5	69.8
Mean	78.4	82.9	77.1	81.1

d1-3B does not win every task. Decider 4B scores higher on MASSIVE intent, BoolQ and XNLI, and d1-omni-600M beats d1-3B on Civil Comments. Liquid AI says d1-omni-600M surpasses Decider 2B with a quarter of the parameters.

Liquid AI reports no vision or audio benchmark scores. It says it validated that d1-3B retains the vision capabilities of its backbone, and that audio decision benchmarks remain an open problem.

Latency

Liquid AI measured d1-3B with NVIDIA on several devices. Speed figures are single-question latency, and no speed numbers were published for d1-omni-600M.

Device	One question	384px image	64 states, packed
Jetson AGX Thor	16 ms	35 ms	262/s
Jetson AGX Orin 64 GB	26 ms	83 ms	110/s
Jetson Orin Nano	50 ms	202 ms	38/s
Apple M5 Pro	30 ms	62 ms	78/s
NVIDIA RTX 4090	8 ms	17 ms	475/s
AMD MI325X	9 ms	18 ms	1,106/s

Processing a 3.4K-token state takes 220 ms on AGX Thor and 1,640 ms on Orin Nano. Three questions over one state cost about 1.3x the time of one on edge devices.

Availability

Both models are on Hugging Face as open weights, with a demo in the System One Arcade Space. They require transformers>=5.14 and trust_remote_code=True. The API exposes named questions of type yes/no, choice or score through system_one and system_one_batch. Context window, training cutoff and license terms were not stated in the announcement.

What this means

The design targets classification, routing and triage workloads, where a generative model's token-by-token decoding adds latency without adding value. Sub-50 ms answers on a Jetson Orin Nano make on-device decisions plausible without a cloud call.

The headline claims need outside validation. The Decision Index is Liquid AI's own benchmark, and the vision split of the newer v0.3 is private. Decider 4B still beats d1-3B on three of seven public tasks, so the advantage is not uniform. The absence of vision and audio scores also leaves multimodal quality unquantified. Teams should test on their own decision tasks before replacing existing classifiers.