Decision Models vs. LLMs as Rubric Judges: Cheaper, Faster, and Wrong in the Same Places
LLM judges now score outputs against rubrics, in benchmarks and as training rewards. Decision models return probabilities over permitted answers with a confidence score but no free text. Two such models are Jev and an open reproduction, Perplexity's Decider. We compare each with three flash-tier LLM judges on nine panels from seven benchmarks with human judgments, giving every judge identical criterion texts. The LLM judges run in two setups, reading a whole rubric at once or one criterion at a time. Our findings suggest both decision models can often stand in for them. The LLM judges cost 16 to 357 times as much and take 5 to 350 times as long, yet in both setups each decision model trai...
| Authors | Delip Rao, Chris Callison-Burch |
|---|---|
| Primary category | cs.CL |
| Published | 24 September 2026 |
| Last updated at source | 13 October 2026 |
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