AI Weekly Malaysia

Claire Vo walks through Jev, TypeSafe AI's "decision model" that returns type-safe structured values (a choice, a score, a probability) instead of generated text, priced at 4 cents per million input tokens with no output charge. She reports running it on five projects in a week: categorizing 1,700 PRs for 9 cents, a meta-analysis of her own Claude and Codex sessions, Gmail triage, the ChatPRD product insights graph (1,100 signals, 200,000 classifications), and a live dashboard built from 4,500 YouTube comments. She also says she stopped using Jev alone and now pairs it with other models such as Gemini 3.5 Flash-Lite.

Why: If your pipeline spends money on an LLM just to bucket, label, or score things, this is a concrete alternative pricing shape to test: input-only billing with no output charge, claimed at 4 cents per million input tokens and 9 cents for 1,700 PR categorizations. The practical move is to take one existing classification or triage job you already run and benchmark a structured-output decision model against your current model on cost and label accuracy, rather than assuming general chat-model pricing. Note this is a launch-week episode with a sponsor segment, so the numbers are the author's own reported results, not an independent benchmark, and there is no Malaysia or Southeast Asia angle in the text.