The new decision model scores 65 by refusing to make things up, even if it gets fewer answers right
Liquid AI has a new leaderboard champion, and it won by knowing what it doesn’t know.
The company’s d1-ultra model now sits first on the Artificial Analysis Decision Index with a score of 65. It pairs a high non-hallucination rate with lower accuracy, which is a trade-off some businesses will find very appealing.
What d1-ultra actually does #
Liquid AI announced d1-ultra on October 15, 2026. It is a multimodal decision model, meaning it handles both text and images.
Instead of generating free-form text, d1-ultra looks at a predefined set of answer options and assigns each one a calibrated probability. Picture a multiple-choice exam where the student also writes how confident they are next to every bubble.
Calibration is the key word. A well-calibrated model that says it’s 80% sure should be right about 80% of the time, not 40%.
That matters for the use cases Liquid AI is targeting. The company points to tasks like visual defect detection and insurance claim verification, plus policy checks.
In those settings, a model that flags uncertainty is useful. Confident cases get automated, and shaky ones get handed off to a human operator. That is the workflow Liquid AI is pitching.
Under the hood, the model processes text and images together in a single forward pass, then outputs its odds without generating free-form output tokens.
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The numbers behind the ranking #
d1-ultra’s 65 on the Decision Index puts it 8 points ahead of the prior leader, Jev 1.14, which scored 57.
An 8-point gap on a freshly launched index is a comfortable lead. It is also a lead on a leaderboard that has existed for roughly two days, since the Decision Index began public operations on October 13, 2026.
The index evaluates models across four distinct assessments:
- Knowledge and hallucination: does the model know facts, and does it avoid inventing them
- Reasoning: can it work through multi-step problems
- Tool usage: can it operate within systems that call external functions
- Safety classification: can it correctly sort content by risk
Separately, Liquid AI ran its own 112-evaluation suite. According to the company, d1-ultra outperformed both the earlier d1 model and Jev 1.14 across all eight evaluation categories in that suite.
Liquid AI also says d1-ultra posted the lowest calibration error across those 112 evaluations.
Pricing, languages and availability #
d1-ultra is available through the Liquid API at $0.042 per million input tokens.
The model supports 44 languages.
Integrations with Vercel and OpenRouter are expected, though Liquid AI has said those will be announced later. For now, the Liquid API is the front door.
What this means #
The most important caveat is verification. Independent verification of Liquid AI’s internal evaluations has not been established, which leaves some doubt about how firm the absolute rankings on the new index really are.
Still, the design choice behind d1-ultra is worth taking seriously. In regulated, high-stakes workflows, a wrong answer delivered confidently can cost far more than no answer at all.
An insurer doesn’t need a model that approves every claim with swagger. It needs one that clears the obvious cases and escalates the weird ones. A model trading some accuracy for fewer hallucinations may be the better fit there, even if it looks weaker on a pure accuracy chart.
Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our