What does this equation mean?

Collecting every distinction above into one expression makes explicit what a bare price comparison assumes without saying so. For vendor i , let $p^{\text{in}}_i$ and $p^{\text{out}}_i$ be the published input and output rates; let $\kappa_i$ be a tokenizer expansion factor, the number of tokens vendor i ’s own tokenizer needs to encode one fixed reference passage, normalised so $\kappa_i$ = 1 for whichever vendor is used as the baseline; let $h_i$ be the fraction of input served from cache and $\gamma_i$ the cache-read multiplier; and let $\rho_i$ be the ratio of total output tokens generated, visible answer plus hidden reasoning, to the visible answer alone. A workload’s realised cost per completed task is then approximately $C_i \;=\; \kappa_i\Big[(1-h_i)\,p^{\text{in}}_i + h_i\, p^{\text{in}}_i\,\gamma_i\Big] \;+\; \rho_i\, p^{\text{out}}_i$. Reading $p^{\text{in}}_i$ and $p^{\text{out}}_i$ off a rate card and comparing them directly across vendors is equivalent to assuming $\kappa_i$ , $\gamma_i$ , and $\rho_i$ are all equal to each other and to 1 across every vendor in the comparison. This article has just shown that one of those three, $\gamma_i$ , genuinely does converge close to 0.1 across all three companies’ published cards — the one term a naive comparison happens to get right by accident. The other two do not converge, are not published as clean multipliers by any vendor, and vary by content type and by task in ways that only measurement on the caller’s own workload can pin down. A price comparison that reports $p^{\text{in}}_i$ and $p^{\text{out}}_i$ alone has silently set $\kappa_i$ = $\rho_i$ = 1 for every vendor, an assumption none of the sourcing above supports.