Liquid AI has released d1-ultra, its most capable decision model to date. The Liquid AI d1-ultra decision model reads text, images, or both. It answers typed questions with a probability for each possible answer. It does this in one forward pass and generates no output tokens. For developers, that means classification, routing and scoring calls without paying for generated text. It is available now on the Liquid API at $0.042 per million input tokens.
TL;DR
- Size: Parameters and context length not disclosed. Built on a larger open-weight backbone than d1.
- Runs on: Hosted Liquid API only. No downloadable weights or quantized variants announced.
- Performance: Leads all 8 categories in Liquid’s 112-evaluation suite, per Liquid.
- Best: 65 on the Artificial Analysis Decision Index, ranked #1 (Liquid-reported).
- Worst: No public per-benchmark breakdown or parameter count yet; claims are vendor-reported.
What is a decision model?
A decision model answers bounded questions with probabilities instead of free text. Liquid’s decision model docs describe 3 question types:
- Noul: a yes/no question that returns a probability between 0 and 1.
- Choice: one label from a set, with a probability per label.
- Score: a probability-weighted position on an ordered rubric.
The docs recommend decision models for routing, moderation, guardrails, triage and LLM-as-judge replacement. They recommend a language model for summarization, chat and code generation.
How does d1-ultra work?
Each request carries a state (the context), optional images, and 1 or more typed questions. The model evaluates every question against the same input in 1 call. The d1 API reference shows responses with output_tokens always at 0.
Liquid AI team states that d1-ultra uses a larger open-weight backbone than d1, along with heavy Liquid-specific customizations. Combined with Liquid’s decision training pipeline, the gains are largest on knowledge tasks.
Liquid AI team built d1-ultra for messy cases. Its examples include spotting a circuit board defect against a spec. Another is checking a damage claim against the customer’s photo.
