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Liquid AI's d1 represents a significant shift from generative text to structured decision-making, offering calibrated probabilities for tasks like classification and scoring without incurring output token costs. This architecture directly addresses efficiency bottlenecks by offloading deterministic logic from general-purpose LLMs to specialized models that return zero generated tokens. The release highlights an emerging trend where AI infrastructure prioritizes precise, low-latency outcomes over open-ended text composition for enterprise workflows.

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Liquid AI Releases d1: A Decision Model That Returns Calibrated Probabilities With Zero Output Tokens

MarkTechPostSeptember 29, 20264 min read

Liquid AI has released d1, a decision model built for structured choices instead of text generation. You give it context and a set of typed questions. It returns calibrated probabilities across a fixed set of outcomes in a single call, with zero generated tokens. The target is the work many teams still send to general […] The post Liquid AI Releases d1: A Decision Model That Returns Calibrated Probabilities With Zero Output Tokens appeared first on MarkTechPost.

Liquid AI has released d1, a decision model built for structured choices instead of text generation. You give it context and a set of typed questions. It returns calibrated probabilities across a fixed set of outcomes in a single call, with zero generated tokens. The target is the work many teams still send to general LLMs: classification, ticket routing, scoring, moderation, reranking and LLM-as-judge checks. Is it deployable? Yes, today, as a hosted…

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