Liquid AI released d1-omni-600M, an experimental open-weight decision model built on the LFM2.5-Encoder-350M bidirectional encoder. It takes a state as text or JSON plus either images or up to 30 seconds of speech, and returns typed answers to yes/no, pick-one-of-named-options and ordered score questions in one forward pass with zero output tokens. It has 587M total parameters (381M shared trunk and decision head, 94M SigLIP2 vision encoder, 112M FastConformer audio encoder) and a 16,384 token context that covers text, image and audio positions together. Weights are on Hugging Face under a non-standard license and it scores 15.95 on Decision Index v0.2.1.
A workable 0.59B-parameter dense decision model from Liquid AI. Treat the modality benchmarks above as the leading indicator of fit — composite scoring across modalities is still maturing. Newly released, so production-readiness is still being shaken out.
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| Device | Grade | VRAM |
|---|---|---|
| ACEMAGIC M1A Pro (i9-13900HK + ARC A770)ACEMAGIC | SS | 0.9 GB |
| Acer Veriton GN100 AI MiniAcer | SS | 0.9 GB |
| AMD Instinct MI300XAMD | SS | 0.9 GB |
| AMD Instinct MI325XAMD | SS | 0.9 GB |
| AMD Instinct MI355XAMD | SS | 0.9 GB |
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d1-omni-600M is a 587M parameter decision model from Liquid AI's open d1 family. Questions follow the Decision Index schema with type, instructions and criteria, and each call returns typed answers plus input/output token usage where output tokens are always zero. It handles text and images or text and audio, not both in one request, and is not a chat model. Recommended uses include routing and triage, moderation, intent and topic classification, voice-command routing, extraction checks, reranking, agent guardrails and visual inspection.

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