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TypeSafe AI's Jev Turns Classification Into a Cheap API Call
TypeSafe AI released Jev, a 'System One' or 'decision model' that takes text input and returns floating point scores (yes/no confidence, choice probabilities, or ratings) instead of generated text. It costs $0.042 per million input tokens with free output, undercutting GPT-5 Nano, and evaluates many questions in parallel.
What changes for operators — For a B2B team running lead scoring, support ticket triage, spam filtering or search relevance ranking, Jev's format maps directly onto those workflows: feed it a customer record or ticket text plus a set of yes/no or scored questions, and get back structured confidence numbers cheaply enough to run on every record rather than a sampled subset. The catch is that Jev gives no explanation for its scores, so anything touching hiring, credit, or other high-stakes decisions needs structured evals before deployment, and even lower-stakes uses like ticket prioritization should be spot-checked for skewed outputs.