Try LFM • Docs • Discord

d1-3B

d1-3B is a 3B parameter decision model built on LFM2.5-VL-3B. You give it a state (text, JSON, images, or a mix) and a set of questions. It returns calibrated, typed answers in one forward pass with zero output tokens.

• Best decision model under 10B on the Decision Index 0.2.1: 48.57, ahead of every 4B and 9B model and of Decider 35B-A3B (47.11).
• Multimodal: images and text in the same state. It scores 74.1 on 11 public image benchmarks (LFM2.5-VL-3B: 73.9).
• Fast: 8 ms a decision on an NVIDIA RTX 4090, 9 ms on an AMD MI325X, 30 ms on an Apple M5 Pro.

Find more information about open d1 in our blog post.

💻 Demos: Try d1-3B in a Hugging Face space without any setup: Open d1 Arcade: Collection of 10 demos using d1-3B

🗒️ Model Details

Model	Parameters	Description
LFM2.5-VL-3B	3.1B	General-purpose vision-language model (base)
d1-3B	3.1B	Post-trained for single-pass, calibrated decisions

d1-3B is a multimodal decision model with the following features:

• Total parameters: 3.12B
• Vision encoder: SigLIP2 NaFlex shape-optimized 400M
• Context length: 32,768 tokens
• Vocabulary size: 128,000

We recommend d1-3B wherever a pipeline needs a yes/no, a pick from named options, or a rating: routing and triage, moderation, intent and topic classification, extraction checks, reranking, LLM-as-a-judge scoring, agent guardrails, and visual inspection. It is not a chat model and does not write text.

🏃 How to use

Install the dependencies (requires transformers>=5.14):

pip install "transformers>=5.14" torch torchvision pillow

The model ships its own code, so load it with trust_remote_code=True:

import torch
from transformers import AutoModel
from transformers.image_utils import load_image

device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
dtype = torch.float32 if device == "cpu" else torch.bfloat16
model = AutoModel.from_pretrained("LiquidAI/d1-3B", trust_remote_code=True, dtype=dtype).to(device)

# Text: several named questions over one state, answered in one pass
questions = {
    "refund": {
        "type": "noul",
        "instructions": "Is the customer asking for a refund?",
    },
    "team": {
        "type": "choice",
        "instructions": "Which team should handle this?",
        "criteria": {
            "billing": "Charges, refunds, invoices",
            "technical": "App or site faults",
            "fraud": "Suspected unauthorised use",
        },
    },
    "urgency": {
        "type": "score",
        "instructions": "How urgent is this?",
        "criteria": ["Can wait", "Today", "Blocking the customer now"],
    },
}
print(model.system_one("I was charged twice this month, please refund one of them.", questions))

# Image: the photo is the whole state
image = load_image("http://images.cocodataset.org/val2017/000000039769.jpg")  # two cats on a sofa
cats = {
    "type": "choice",
    "instructions": "How many cats are there?",
    "criteria": {"one": "One", "two": "Two", "more": "Three or more"},
}
print(model.system_one(None, {"cats": cats}, images=[image]))

# Batch: many requests, packed together with no padding
tickets = ["Where is my parcel? It was due Monday.", "The app crashes when I open settings."]
print(model.system_one_batch([(t, {"team": questions["team"]}) for t in tickets]))

call
system_one(state, questions, images=None)	Named questions over one state, in one pass. The state and its images are read once for all questions.
system_one_batch([(state, questions[, images]), ...])	Many requests, packed with no padding.

A state is a string, any JSON value, or None when the images are the whole state.

Questions and answers

Questions follow the Decision Index schema: type, instructions, and criteria.

type	criteria	answer fields
noul: yes or no	optional: {"true": "...", "false": "..."} to define each side	noul: P(yes)
choice: one of named options	{name: description}	choice, confidence, probabilities
score: 2 to 10 ordered levels	a list of level descriptions, lowest first	score (the expected level), confidence, probabilities, legend

Each call returns {"answers": {name: answer}, "usage": {"input_tokens": n, "output_tokens": 0}}.

⚡ Speed

Warm calls, one request at a time: a single question, three questions over one state, a 3.4k-token state and a 384 px image. The last column is throughput with 64 states packed into one pass.

Edge Inference

We measure latency on an Apple M5 Pro and, in collaboration with NVIDIA, on an NVIDIA Jetson AGX Thor, a Jetson AGX Orin 64 GB and a Jetson Orin Nano.

	one question	3 questions, one pass	3.4k-token state	384 px image	64 states, packed
Apple M5 Pro (mps)	30 ms	41 ms	640 ms	62 ms	78 / s
NVIDIA Jetson AGX Thor	16 ms	20 ms	220 ms	35 ms	262 / s
NVIDIA Jetson AGX Orin 64 GB	26 ms	35 ms	560 ms	83 ms	110 / s
NVIDIA Jetson Orin Nano	50 ms	73 ms	1640 ms	202 ms	38 / s

GPU Inference

We measure latency on an NVIDIA RTX 4090 and an AMD MI325X, in bf16, median of 20 runs.

	one question	3 questions, one pass	3.4k-token state	384 px image	64 states, packed
NVIDIA RTX 4090	8 ms	21 ms	102 ms	17 ms	475 / s
AMD MI325X	9 ms	14 ms	44 ms	18 ms	1,106 / s

On NVIDIA GPUs, model.compile(mode="reduce-overhead") runs single questions as CUDA graphs (the RTX 4090 row uses it). Without it, a single question takes 16 ms. The first call with a new shape pays for kernel selection or compilation, so warm up the shapes you serve.

📊 Performance

All results are on public benchmarks.

Decision Index 0.2.1

We scored d1-3B with the official scorer (not a leaderboard submission). All other rows come from the public leaderboard v0.2.1.

Model	Size	Decision Index	Knowledge	Language	Retrieval	Tools	Arts
Winnow-12B	12B	50.02	33.8	56.0	54.0	71.0	30.0
d1-3B	3B	48.57	23.8	56.4	52.8	74.5	36.3
Decider 35B-A3B	36B	47.11	31.8	55.5	54.7	56.5	32.6
JPT-9B	9.7B	46.89	31.7	56.7	44.6	67.0	28.6
Decision 1.0 Lux	9.7B	43.49	30.9	48.0	50.0	57.2	26.4
JPT-4B	4.7B	43.04	28.7	52.5	45.0	57.2	25.8
Jet v6.2	4.7B	42.60	28.7	43.9	48.2	62.9	27.0
Decider 4B	4.7B	40.70	25.7	46.0	44.7	58.6	25.0
Winnow-E4B	8.0B	39.89	22.3	45.1	43.8	62.5	22.8
Decider 2B	2.3B	28.97	14.9	32.6	37.3	42.4	14.6

Benchmarks as decisions

Besides the Decision Index, we added a few other internal evaluations based on public benchmarks.

Benchmark	d1-3B	Decider 4B	Decider 2B
SQuAD 2.0	85.3	76.0	67.7
Civil Comments	93.0	92.8	93.6
MASSIVE intent	87.3	88.3	81.1
HelpSteer2	36.7	42.0	32.0
PubMedQA	66.0	63.3	65.7
BoolQ	86.7	89.0	87.3
XNLI	85.0	88.6	85.0
PAWS-X	76.9	69.8	59.5
Mean	77.1	76.2	71.5

d1-3B also scores 71.8 on DecisionBench (eng v1, all 23,900 rows) and 69.3 on Fast Decisions (dev split).

Vision

Eleven public image benchmarks, read as decisions over each benchmark's options (at most 1,000 rows each), compared with the base model:

Benchmark	d1-3B	LFM2.5-VL-3B
AI2D	79.9	80.9
BLINK	59.2	58.7
CV-Bench	82.1	87.6
HallusionBench	65.3	65.0
MMBench	84.9	84.3
MME	82.1	82.4
MMStar	59.9	61.2
MMVP	77.0	73.7
POPE	88.5	90.1
VisualWebBench	71.4	78.3
VL-RewardBench	65.0	50.9
Mean	74.1	73.9
ImajevBench (dev and calibration, 253 rows)	64.0	66.8

With the images removed, the same questions score 45.1, so the answers come from the images.

📬 Contact

• Got questions or want to connect? Join our Discord community
• If you are interested in custom solutions with edge deployment, please contact our sales team.

Citation

@article{liquidAI2026opend1,
  author  = {Liquid AI},
  title   = {Open d1: Edge decision models for text, vision, and audio},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {https://www.liquid.ai/blog/d1-open},
}

@article{liquidai2025lfm2,
  title   = {LFM2 Technical Report},
  author  = {Liquid AI},
  journal = {arXiv preprint arXiv:2511.23404},
  year    = {2025}
}