Frontier quality at 1% of the cost

Airbnb's production benchmark of a content-policy classifier, and the most credible production-scale independent data on Jev yet: on a 10,000-example held-out set at thousands of RPM, Jev is "the most accurate off-the-shelf model we tested under 100 ms" and beats GPT-6 Luna at equal cost — but a fine-tuned Gemma4-E2B scoring via logprobs beats everything on all three axes (F1 0.563 → 0.902 at 35 ms median, ~1% of frontier cost), and a 2,700-word prompt fails to close the gap for any deployable model. Three findings worth pinning: thresholds beat verdicts at production scale (the class's whole thesis, confirmed); fine-tuning moves the quality-latency frontier instead of trading along it; and Jev's cost quadrupled under the long prompt because at evaluation time its API offered no cached-input discount — a gap both OpenAI's Decisions GA and Morph Reflex now price.