Anyone curious about Decision Models?? I spent some time this week digging in to the details. Unbelievable proliferation in 3 weeks. https://opendoorlearningai.com/articles/113-decision-models-in-3-weeks/ TL;DR - A decision model takes some evidence plus closed questions and returns typed answers with probabilities. It writes no prose. A decision costs a tiny fraction of a cent and comes back in tens to hundreds of milliseconds. - One request can carry many questions. They run in parallel and independently, so nothing chains inside a call. - “113 entries” overstates the variety. 70 of the 112 open entries are built on Qwen, 18 on Gemma 4, and 20 are stock models with a decoding trick and no new weights. - On the independent Decision Index, Perplexity Decider v1.1 ranks first on its own, and Fastino GLiDE, Jev and Torchcast tie for second. Calibration, price and where you can run it separate the leaders more than accuracy does. - Measured fairly, the 27B models answer in 0.10 to 0.14 s on one local GPU, and hosted APIs timed by independent testers mostly land between 0.1 and 0.6 s. - Laya is one of the weakest open options. Every hardware tier, down to a laptop, has open models scoring 6x to 14x higher. - My proposed inbox setup: one script polls, filters, asks for four facts per email and applies rules, and the LLM runs only for mail that needs writing. Estimated $353 a month down to $28. It isn’t running yet; results will follow in a separate article.