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The Price of Intelligence: A Quality-Adjusted Price Index for AI Services

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  • Louis Yiven Zhu

Abstract

How fast AI inference prices have fallen depends on how an index holds quality constant. We build quality-adjusted price indices for text and code inference sold through APIs, joining 21,024 posted prices to a capability scale fitted to 4,605 benchmark scores. From January 2025 to July 2026, matched-model methods record an annual fall of 0.07 in log price per token. Our pre-specified hedonic index falls at 0.51 a year in logs (95% interval 0.11 to 0.89), entirely through the rising capability of the models on sale. Across three treatments of measurement error and two anchor sets it runs from 0.34 to 0.89. Excluding six anchors flagged for possible contamination leaves model rankings intact (rank correlation 0.998) yet lowers the rate by 0.40 to 0.51 under two treatments. Agreement on rankings therefore does not license claims about levels or increments. Per completed task, the data do not establish a quality-adjusted decline.

Suggested Citation

  • Louis Yiven Zhu, 2026. "The Price of Intelligence: A Quality-Adjusted Price Index for AI Services," Papers 2608.29843, arXiv.org, revised Nov 2026.
    • Handle: RePEc:arx:papers:2608.29843
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