decider-2b
The most-downloaded open-weight decision model in the post-Jev wave. A Qwen3.5-2B decoder fine-tuned to emit typed decisions, and the largest member of a three-model family.
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What it is
decider-2b is a decoder-based decision model: Qwen3.5-2B fine-tuned so that the answer to a
typed question comes out as the typed value rather than as prose that has to be parsed. It sits
in the middle of a family that also includes decider-0.8b and a decider-35b-a3b mixture-of-experts
variant.
It is the most pulled open-weight model in this directory by a wide margin, with far more downloads than likes. That ratio is what a model being wired into things looks like, as opposed to a model being admired.
Why the architecture matters here
A decoder produces a decision by generating tokens, which means the probability you read off it is a token probability, not a calibrated estimate over the decision space. Getting a usable confidence number out of one takes constrained decoding plus a normalisation step over the option set, and different implementations do this differently. Two decoder-based decision models can report incomparable confidences while both being honest.
That is the specific thing we want to put a number on: whether a 2B decoder can hold calibration while matching an encoder a tenth its size on accuracy.
A public number is a shortlist, not a decision
The only benchmark that settles which model you ship is one built from your own historical decisions. We freeze that test set, run the candidates against it, and hand back the threshold your error budget supports.