Simon Willison on Jev: a 'decision model' that returns floats instead of text

Simon Willison · rss · 2026-09-22

Simon Willison reviews Jev from TypeSafe AI — billed a "System One model," better named a "decision model": text in, but floating-point numbers out for classifications, yes/no questions, scores, and confidences.

Pricing: input-only billing, free output, $0.042/million tokens — cheaper than GPT-5 Nano ($0.05/M).
Three question types: "Noul" (Bernoulli) yes/no with 0-1 confidence; choice questions with probability distributions; score questions along custom ranges. One state takes many questions in parallel, with latency similar to one.
Use cases: classification tasks — spam detection, labeling, ranking; Willison tried search reranking (BM25 top-100 rescored by Jev).
Black-box concerns: even more opaque than LLMs, no justifications, bias risks (he worries about ranking job applicants; its "good city?" Bay Area experiment rated Cupertino top, East Palo Alto bottom). Evals matter more, though running thousands of prompts costs cents.
Community hacks: jevchat, jev-leftpad, jev-2048.
Open-weight clones: Kev (Qwen 3.5-based, 0.8B/4B/9B) and a JevBench benchmark — impressive activity within a week of launch.

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Simon Willison on Jev: a 'decision model' that returns floats instead of text

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