LiquidAI d1-3B Explained: Architecture, Speed, and Cost

LiquidAI d1-3B is a 3.12-billion-parameter decision model created by Liquid AI that answers binary, categorical, and scoring questions in a single forward pass with zero output tokens. The model weights are downloadable directly on Hugging Face under the lfm1.0 license. Liquid AI publishes no price for the weights, so your cost is the hardware you run them on.

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Built on the LFM2.5-VL-3B multimodal architecture, LiquidAI d1-3B integrates a 400-million-parameter SigLIP2 NaFlex vision encoder, a 32,768-token context window, and a 128,000 vocabulary size. The pipeline accepts images, text passages, JSON, or multimodal combinations across 16 supported languages.

What LiquidAI d1-3B Is and What It Costs

LiquidAI d1-3B is a 3.12-billion-parameter multimodal decision model built to answer structured classification questions in a single computational pass without outputting generative text. The model uses the LFM2.5-VL-3B base architecture, incorporating a SigLIP2 NaFlex shape-optimized 400-million-parameter vision encoder that processes visual inputs alongside text. Its context capacity reaches 32,768 tokens with a 128,000 vocabulary size. The state can be text, JSON, images, or a mix.

Language support spans 16 distinct languages, specifically Arabic, Chinese, English, French, German, Hindi, Indonesian, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Thai, and Vietnamese. The underlying pipeline is classified as image-text-to-text, meaning it consumes images and text states to return typed answers.

In terms of pricing, Liquid AI publishes no price on the model card. The weights are publicly accessible on Hugging Face under the lfm1.0 license. Consequently, your financial investment depends entirely on the local hardware or cloud infrastructure provisioned to host the 3.12B parameters.

• Base Architecture: LFM2.5-VL-3B with 3.12 billion total parameters.
• Vision Components: SigLIP2 NaFlex shape-optimized 400M vision encoder.
• Context and Token Capacity: 32,768-token context window and a 128,000 vocabulary size.
• Language Coverage: 16 languages including English, Spanish, Chinese, German, and Japanese.
• Software Cost: Liquid AI publishes no price for the weights, so your cost is the hardware you run them on under the lfm1.0 license.

How a Decision Model Differs From a Chat Model

LiquidAI d1-3B does not operate as a conversational chat model and does not write generative text.

LiquidAI d1-3B provides a direct decision interface called system_one. The user provides an environmental state composed of text paragraphs, JSON structures, image files, or a mix alongside an explicit set of named questions. The model processes the full context and outputs calibrated, typed answers in a single forward pass without generating conversational output tokens.

The model card demonstrates three primary question types supported by the architecture:

• noul: Binary yes or no style determinations evaluated directly against the provided context.
• choice: Categorical selection among defined options according to specific matching criteria.
• score: a rating question.

Speed Benchmarks and the Decision Index Claim

Liquid AI reports 8 ms per decision on an NVIDIA RTX 4090, 9 ms on an AMD MI325X, and 30 ms on an Apple M5 Pro. According to performance data published by Liquid AI, d1-3B completes a decision in 8 milliseconds on an NVIDIA RTX 4090 GPU, 9 milliseconds on an AMD MI325X accelerator, and 30 milliseconds on an Apple M5 Pro chip.

On capability evaluations, Liquid AI claims d1-3B is the top decision model under 10 billion parameters on Decision Index 0.2.1. Liquid AI records a score of 48.57 for d1-3B, stating that it outperforms all evaluated 4B and 9B models on that benchmark. Liquid AI also reports that d1-3B surpasses Decider 35B-A3B, which scored 47.11 on the same evaluation index.

On multimodal evaluation, Liquid AI reports a score of 74.1 across 11 public image benchmarks. This marginally exceeds the 73.9 score reported for its foundational LFM2.5-VL-3B base model. These are Liquid AI's own figures, so test on your own labelled examples.

• NVIDIA RTX 4090 Latency: 8 milliseconds per decision.
• AMD MI325X Latency: 9 milliseconds per decision.
• Apple M5 Pro Latency: 30 milliseconds per decision.
• Decision Index 0.2.1: 48.57 reported by Liquid AI, compared to 47.11 for Decider 35B-A3B.
• Image Benchmarks: 74.1 across 11 public tests, compared to 73.9 for LFM2.5-VL-3B.

Licensing Terms and Commercial Deployment Rules

The repository metadata for LiquidAI d1-3B categorizes its legal distribution under 'other', with the specific license designation labeled as lfm1.0. The model card does not publish commercial terms in the readme summary. Anyone planning a commercial deployment should review the license file directly in the LiquidAI d1-3B Hugging Face repository.

To run open models yourself, see Ollama Explained and llama.cpp Explained. Reviewing the exact lfm1.0 legal terms remains mandatory before embedding d1-3B inside client-facing commercial software.

Eight Business Workflows for LiquidAI d1-3B

The official model card lists eight target operational roles for LiquidAI d1-3B. These applications align with enterprise processes where software pipelines require fast, typed triage rather than conversational narrative text.

These operational workflows apply wherever a pipeline needs a yes/no, a pick from named options, or a rating.

• Support-Ticket Routing and Triage: Routing and triage.
• Content Moderation: Moderation.
• Intent and Topic Classification: Intent and topic classification.
• Extraction Checks: Extraction checks.
• Search and Reranking: Reranking.
• LLM-as-a-Judge Scoring: LLM-as-a-judge scoring.
• Agent Guardrails: Agent guardrails.
• Visual Quality Inspection: Visual inspection: takes images as part of the state.

Implementation Setup, Verification, and Next Steps

Deploying LiquidAI d1-3B requires a modern Python environment configured with specific libraries. The model card specifies dependencies including transformers version 5.14 or greater, torch, torchvision, and pillow. You load the model using the standard Hugging Face AutoModel interface: AutoModel.from_pretrained('LiquidAI/d1-3B', trust_remote_code=True).

Once loaded into memory, you invoke decisions using model.system_one(state, questions), passing the multimodal state alongside your structured question list. Liquid AI lists a Hugging Face space, Open d1 Arcade, with 10 demos. Community discussions regarding the release are also cataloged on the LocalLLaMA Hugging Face thread.

d1-omni was announced alongside d1-3B, per r/LocalLLaMA post titles Reddit LocalLLaMA. The released model card provides technical specifications strictly for d1-3B and omits architectural details for d1-omni. Developers seeking specifications for d1-omni should check Liquid AI's announcement blog for subsequent publications.

Before deploying d1-3B into production routing pipelines, engineering teams should evaluate answer calibration against a golden dataset of internal labeled records. If your workflow requires creative writing, conversational interactions, or narrative reasoning, LiquidAI d1-3B is not the appropriate tool. What would change our recommendation to deploy d1-3B is if your pipeline relies on unstructured generative dialogue or if the lfm1.0 license restrictions conflict with your software redistribution policies. As a concrete next step, download the weights from Hugging Face and test calibration on your own labelled examples to see how well it matches your labels.