What a finished answer costs, not what a token costs: 9 Claude models, 180 requests in two batteries, 2,160 answers priced at list price. Claude Opus 4.6 to 5.5: list price -20%, cost of a finished answer +38% to +91% depending on the task mix. The output-token change is split into tokenizer, visible writing and hidden thinking.
Download the file or read the study for its structure; both are linked above.
Released under CC BY 4.0. You may use it commercially and redistribute it. Attribution is the only condition.
Couey, V. W. (2026). AI Effective Cost Per Answer 2026: What a Finished Answer Costs, Not What a Token Costs. Deep Synthesis. https://doi.org/10.5281/zenodo.23748727
Found an error, need a different cut of this data, or citing it in something? vincent@deepsynthesis.org. Corrections are made against the published file and the DOI is versioned, so a fix is traceable rather than silent.
Time-series of input/output token prices for 12 frontier model families across 32 versions, with cost-per-reference-task normalized per entry.
Per-tool pricing matrix across the Amazon seller SaaS ecosystem (Helium 10, Jungle Scout, Keepa, et al.) with feature-tier delta and effective monthly cost.
Per-claim evidence audit of common standing-desk health and productivity marketing claims, sourced to peer-reviewed and meta-analytic literature.
Pricing and capability index of 30 go-to-market AI tools across vertical and job-to-be-done, with entry monthly cost, pricing model, and verified-source date.