Multimodal open d1 decision models for the edge

Liquid AI has released two open-weight edge-focused decision models, d1-3B and the experimental d1-omni-600M, designed to make structured classifications, choices, and scores in a single forward pass rather than generate text token by token. Built on Liquid Foundation Models, d1-3B accepts text and images and achieved a 48.57 score on the Decision Index 0.2.1, while d1-omni-600M supports text paired with either images or audio and prioritizes a smaller footprint. Across seven public benchmarks, d1-3B averaged 82.9 and d1-omni-600M averaged 78.4, with the latter exceeding a larger Decider 2B model despite having roughly one-quarter as many parameters. NVIDIA testing showed d1-3B could answer individual questions in 16 milliseconds on Jetson AGX Thor, 26 milliseconds on Jetson AGX Orin, and 50 milliseconds on Jetson Orin Nano, while high-end GPUs delivered sub-10-millisecond response times. Both models are available through Hugging Face, with examples demonstrating their use for tasks such as customer-support routing, urgency scoring, image-based counting, and batched requests.

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