Microsoft-Decision-1
Microsoft-Decision-1 is a fast decision-scoring model built for structured outputs that software can immediately act on. It is designed for routing, classification, prioritization, verification, and workflow control across applications, agents, and automated systems. The model is post-trained from Qwen3.5-9B for single-pass decision scoring and, when given a fixed set of answer options, returns a calibrated probability for each one. It supports yes/no, multiple-choice, rating, and rubric-based grading of AI responses and agent actions through a structured API. Microsoft evaluated the model across 36 benchmarks covering routing, ranking, long context, multilingual and out-of-distribution tasks, reasoning, and safety, and reports that it achieved the highest accuracy in that comparison while also delivering very low latency. The model is also designed for robustness, with testing focused on keeping decisions stable when prompts are paraphrased.
Learn more
Laya AI
Laya AI is an open source family of non-autoregressive System 1 decision models built for fast, typed decisions instead of free-form text generation. Given a state and structured questions, Laya evaluates them in a single forward pass and returns outputs that application code can use directly. It supports three decision types: choice, which selects among named options and returns probabilities; score, which places an input on an ordered rubric such as urgency or severity; and noul, which estimates the probability that a proposition is true. Laya is designed for workloads such as agent and model routing, support ticket triage, guardrails, intent classification, risk and fraud scoring, workflow escalation, RAG filtering, moderation, and phishing detection. It can route requests by department, score urgency, flag refund or churn signals, detect policy or abuse conditions, and escalate uncertain cases to a larger model or human reviewer.
Learn more
Clef-flash
Clef-flash is a fast 9B multimodal decision model that turns a state and a schema of typed questions into structured decisions. It can read state information as text, JSON, images, or video and returns a probability for every allowed option of every question. The model supports a 24,576-token context window and vision input, allowing developers to evaluate structured application data and visual content within the same workflow. Clef Flash supports three typed question formats: noul for estimating the probability that a proposition is true, choice for selecting among predefined options with per-option probabilities, and score for evaluating an input against ordered criteria and returning a probability-weighted score. Applications can submit from 1 to 64 questions in a single request, with answers returned under the same question IDs. Example uses include deciding whether a support request is urgent, routing it to the correct team, and scoring the severity of customer impact.
Learn more
Clef
Clef is a 27B multimodal decision model that turns a state and a schema of typed questions into structured decisions. It can read state information as text, JSON, images, or video and return a probability for every allowed option of every question. The model supports a 65,536-token context window and vision inputs, allowing developers to evaluate both structured application data and visual content in the same decision workflow. Clef supports three typed question formats: noul for estimating the probability that a proposition is true, choice for selecting among predefined options and returning per-option probabilities, and score for evaluating an input against ordered criteria and producing a probability-weighted score. Applications can submit between 1 and 64 questions in a single request, with answers returned under the same question IDs.
Learn more
