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Liquid AI releases d1-ultra decision model at $0.042 per million tokens

Liquid AI has released d1-ultra, a multimodal decision model designed to answer bounded questions with probabilities rather than generating free text. The model processes text and images in a single forward pass, producing zero output tokens, which reduces costs for tasks like classification, routing, and moderation. It is currently available via the Liquid API at a rate of $0.042 per million…

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Key points

  • Liquid AI released d1-ultra, a multimodal decision model returning probabilities with zero output tokens.
  • The model is available on the Liquid API for $0.042 per million input tokens.
  • Liquid reports d1-ultra ranks #1 on the Artificial Analysis Decision Index with a score of 65.

According to Liquid AI, d1-ultra builds on a larger open-weight backbone than its predecessor, d1, with customizations that improve performance on knowledge tasks. The company reports that the model ranks #1 on the Artificial Analysis Decision Index with a score of 65, outperforming TypeSafe’s Jev 1.14 across all 112 evaluations in their internal suite. However, specific parameter counts and context lengths have not been disclosed, and the model is only accessible through the hosted API, with no downloadable weights or quantized variants announced.

Pricing is based solely on input tokens, with images costing 1.5 tokens per 32x32-pixel patch. Developers can test the model in the d1 Playground, and support for Vercel AI Gateway and OpenRouter is expected soon. The release targets use cases where precise probability outputs are needed, such as spotting defects in circuit boards or verifying insurance claims against photos.

Model pages: d1 → · Jev 1.14 → · Microsoft-Decision-1 →

The story so far

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Full story from MarkTechPost · by Asif RazzaqOpen source ↗

Liquid AI Releases d1-ultra: A Multimodal Decision Model That Scores 65 on the AA Decision Index

MarkTechPost · 16 October 2026

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This text was published by MarkTechPost and written by Asif Razzaq. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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