d1-3B
by LiquidAI
A 3B open decision model that answers yes/no, choice and rating questions over text and images in one pass, 8 ms on an RTX 4090
LiquidAI/d1-3BDeploy d1-3B
Single-tenantMixpeek has no managed extractor for this model. On a single-tenant deployment you upload the weights and a custom plugin serves them next to the rest of your pipeline.
Overview
d1-3B is Liquid AI's open decision model, released in October 2026 and built on LFM2.5-VL-3B. Give it a state, which can be text, JSON, images or a mix, and a set of named questions, and it returns a calibrated probability for each answer in one forward pass with no generated text.
Liquid reports it as the best decision model under 10B on the Decision Index 0.2.1 (48.57, ahead of a 36B model at 47.11) and fast enough for real-time pipelines: 8 ms a question on an RTX 4090 and 30 ms on an Apple M5 Pro. The weights are free for commercial use only for organisations under $10M in annual revenue.
Architecture
LFM2.5-VL-3B (3.12B parameters, SigLIP2 NaFlex vision encoder, 32,768-token context, 128K vocabulary) post-trained to read each answer directly from the model's distribution over the options. Questions follow the Decision Index schema: a type (noul for yes/no, choice, or score), instructions and criteria. The state and images are read once for all questions in a call.
Mixpeek SDK Integration
# Decision models judge candidates; index and retrieve the content with Mixpeek first.
import requests
docs = requests.post(
"https://api.mixpeek.com/v1/retrievers/ret_your_retriever/execute",
headers={"Authorization": "Bearer API_KEY", "X-Namespace": "ns_your_namespace"},
json={"inputs": {"query": "damaged packaging on arrival"}},
).json()["documents"]Capabilities
- Yes/no, pick-one and 2-to-10-level rating questions, answered as probabilities with zero output tokens
- Several named questions over one state in a single pass
- Text, JSON and images in the same state; 32,768-token context
- Batching that packs many states into one pass with no padding
- Runs on data-centre GPUs, RTX workstations, Apple silicon and Jetson
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| Decision Index 0.2.1 | Score | 48.57 | Model card: LiquidAI/d1-3B (Liquid ran the official scorer itself, without a leaderboard submission; Decider 35B-A3B 47.11) |
| 8 public benchmarks as decisions (SQuAD 2.0, BoolQ, XNLI and others) | Mean | 77.1 | Model card (Liquid's internal evaluation; Decider 4B 76.2) |
| 11 public image benchmarks as decisions | Mean | 74.1 | Model card (base LFM2.5-VL-3B 73.9) |
| DecisionBench (eng v1, 23,900 rows) | Score | 71.8 | Model card (self-reported) |
| One question, NVIDIA RTX 4090, bf16 | Latency | 8 ms | Model card (warm, with model.compile; 16 ms without) |
Performance
Liquid's figures: 8 ms a question on an RTX 4090, 9 ms on an AMD MI325X, 30 ms on an Apple M5 Pro, 50 ms on a Jetson Orin Nano; 475 states a second packed on the 4090. The first call with a new shape pays for compilation, so warm up. We have not measured it.
Common Pipeline Companions
Frequently Asked Questions
What is d1-3B?
An open decision model from Liquid AI. It answers questions about text or images, such as yes or no, which of these options, or how severe on a scale, with a probability for each answer and no generated text.
How fast is d1-3B?
Liquid reports 8 ms for one question on an NVIDIA RTX 4090, 9 ms on an AMD MI325X and 30 ms on an Apple M5 Pro, warm and one request at a time. Packing 64 states into one pass reaches 475 a second on the 4090.
Can I use d1-3B commercially?
Under the LFM Open License v1.0, commercial use is allowed for organisations with less than $10M in annual revenue. Above that you need an agreement with Liquid AI.
How do I use d1-3B with Mixpeek?
Run it on the candidates a Mixpeek retriever returns, to keep, label or reorder them with a probability per item, as in the example on this page. Mixpeek's built-in decision stages call TypeSafe AI's hosted Jev by default.
Specification
Research Paper
Open d1: Edge decision models for text, vision, and audio (Liquid AI)
arxiv.orgBuild a pipeline with d1-3B
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