d1-3B

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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.

Image: image

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

🗒️ Model Details#

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]))

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.

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.

GPU Inference#

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

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.

Benchmarks as decisions#

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

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:

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}
}