Liquid AI Launches d1-3B and d1-omni-600M: Multimodal Decision Models with 8ms Latency

Executive Overview

Liquid AI today announced the launch of two open-weight decision models in the d1 family: d1-3B and d1-omni-600M (an experimental research release). Both are available immediately for download on Hugging Face with a demo in the System One Arcade Space. The d1-3B scores 48.57 on Decision Index 0.2.1, making it the best decision model under 10B parameters, with latency as low as 8ms on RTX 4090 and 16ms on Jetson AGX Thor. These models are built on Liquid Foundation Models (LFMs) and differ fundamentally from generative models because they do not produce tokens but answer queries in a single forward pass, enabling real-time decision applications on edge devices.

Deep-Dive Features & Architecture

The d1 models are built on Liquid Foundation Models (LFMs), which differ fundamentally from generative models because they do not produce tokens but answer queries in a single forward pass. The d1-3B uses the LFM2.5-VL-3B backbone, a vision-language model (VLM) with a decoder-only architecture that accepts text and images. The d1-omni-600M relies on LFM2.5-Encoder-350M, a bidirectional encoder that adds vision and audio encoders to process three modalities, accepting either text and image or text and audio.

In terms of performance, d1-3B achieves 48.57 on Decision Index 0.2.1, outperforming all 4B and 9B models and the Decider 35B-A3B which scored 47.11. On the average of seven public datasets, d1-3B reaches 82.9 versus 81.1 for Decider 4B, while d1-omni-600M scores 78.4, surpassing Decider 2B (77.1) with only a quarter of the parameters. It has been verified that d1-3B retains the vision capabilities of its base model, and d1-omni-600M processes all three modalities, but the company does not publish vision or audio benchmarks because Decision Index v0.3 includes only a private vision slice and audio benchmarks remain an open problem.

Benchmark & Competitive Performance

In a direct comparison on seven datasets, d1-3B outperforms Decider 4B on SQuAD 2.0 (83.3 vs 76.0), Civil Comments (93.3 vs 92.8), PubMedQA (68.3 vs 63.3), and PAWS-X (76.4 vs 69.8), while Decider 4B leads on BoolQ (89.0 vs 86.3), XNLI (88.6 vs 85.6), and MASSIVE intent (88.3 vs 86.9). The d1-omni-600M outperforms Decider 2B on SQuAD 2.0 (74.0 vs 67.7), Civil Comments (95.8 vs 93.6), MASSIVE intent (86.1 vs 81.1), and PAWS-X (79.5 vs 59.5), with slight declines on PubMedQA (61.3 vs 65.7), BoolQ (77.7 vs 87.3), and XNLI (74.7 vs 85.0).

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Industry Impact & Enterprise Adoption

The launch of d1-3B and d1-omni-600M signals a significant shift toward real-time decision-making at the edge. With latency as low as 8ms on consumer-grade GPUs and 16ms on embedded devices like Jetson AGX Thor, these models enable applications in robotics, autonomous systems, and interactive AI that require immediate responses without cloud dependency. The open-weight nature and free availability on Hugging Face lower the barrier for enterprises to integrate advanced decision capabilities into their products, potentially accelerating adoption in manufacturing, healthcare, and customer service. The strong multilingual performance, including support for Arabic via XNLI and MASSIVE benchmarks, positions Liquid AI's models for global deployment, though regional availability details remain unspecified.

Conclusion

Liquid AI's d1-3B and d1-omni-600M represent a leap forward in efficient, multimodal decision models. By achieving top-tier Decision Index scores and millisecond latencies on edge hardware, they empower developers to build responsive AI systems that operate locally. The open-weight release on Hugging Face fosters innovation and accessibility, while the competitive benchmarks against larger models demonstrate the efficiency of the LFM architecture. As the company continues to refine its Decision Index and expand modality support, these models are poised to become foundational tools for real-time AI applications across industries.

Media Source: Hugging Face | Official Company Statement: Original Source | Fact Verification & Analysis: AI Tools Oasis