Liquid AI released d1-ultra, a multimodal decision model scoring 65 on the AA Decision Index.
Liquid AI released d1-ultra, described as a multimodal decision model that scores 65 on the AA Decision Index. The supplied page body was a cookie-consent banner, so architecture, parameter count, availability, and additional benchmark results were not included.
Liquid AI released multimodal decision model d1-ultra.
It scores 65 on the AA Decision Index.
No size, weights, or further benchmarks were in the supplied text.
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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.
How does d1-ultra perform on benchmarks?
Liquid AI compared d1-ultra against its earlier d1 and TypeSafe’s Jev 1.14 across 112 evaluations. According to Liquid AI’s announcement, d1-ultra outperforms Jev 1.14 across all tasks at the same price.
On the independent Artificial Analysis Decision Index, Liquid reports d1-ultra ranks #1 with a score of 65. The index averages 4 evaluations at 25% each:
AA-Omniscience-Decision: knowledge and hallucination
HLE-MultiChoice: reasoning
When2Call: tool calling
WildGuardTest: safety classification
For context, Artificial Analysis’s Oct 13 launch article listed Jev 1.13 first at 58. GLiDE and Microsoft-Decision-1 followed at 54 each.
Billing covers input tokens only. Per Liquid’s pricing notes, images cost 1.5 tokens per 32×32-pixel patch. A 1024×1024 image therefore costs 1,536 tokens. Each question is billed as its own prompt, including its text and all images. So 3 questions on 1 image bill that image 3 times.
The endpoint docs cap requests at 8 images and 10,000 image patches. The full JSON body must stay under 4.5 MB.
Key Takeaways
d1-ultra returns probabilities for text and image decisions with 0 output tokens.
Liquid reports a #1 score of 65 on the Artificial Analysis Decision Index.
It costs $0.042 per 1M input tokens, the same rate as TypeSafe’s Jev.
Liquid claims the lowest calibration error in its tests, useful for routing thresholds.
Size, backbone and weights remain undisclosed; access is API-only.
Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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