🤖 AI Summary
This study addresses the absence of a standardized metric to evaluate the token expenditure efficiency of organizations deploying artificial intelligence, which hinders meaningful cost-effectiveness assessments. To bridge this gap, the paper introduces the Token Efficiency Index (TEI)—a novel composite benchmarking indicator that integrates three dimensions: cache hit rate, cache amortization ratio, and proportion of high-end model usage. Through normalization and aggregation via equal weighting combined with Benefit-of-the-Doubt data envelopment analysis (DEA) and a robust order-m extension, TEI enables interpretable, cross-organizational, and cross-model efficiency evaluations. The index yields a 0–100 score, peer percentile ranking, and frontier gap analysis, thereby quantifying potential cost savings and offering organizations a transparent, actionable pathway to optimize AI-related expenditures.
📝 Abstract
As artificial intelligence (AI) adoption accelerates across tech giants, AI-native startups, and non-technical organizations alike, a deceptively simple question remains hard to answer: is that spending efficient? AI consumption is priced by tokens, and costs vary by token type (input, output, reasoning) and model type, with usage ranging from a few hundred tokens for simple queries to over a million for multi-step agentic tasks. This variance makes cost comparison, both within and across organizations, difficult without a standardized framework.
We introduce the Token Efficiency Index (TEI), a peer-benchmarked composite indicator that condenses token spend efficiency into a single 0-100 score. The TEI ingests an organization's AI usage data, independent of the underlying provider, and computes three direction-aware metrics: cache hit rate, cache amortization ratio, and premium model share. These are normalized to a common scale and aggregated via an equal weights composite and a Benefit-of-the-Doubt (BoD) Data Envelopment Analysis (DEA) model, with a robust order-m extension for sparse data. The result is a headline score, a peer percentile, and frontier-gap recommendations with estimated savings. Grounded in established methods from composite indicator and DEA literature, the TEI offers a transparent, interpretable approach to benchmarking AI token efficiency and identifying opportunities to optimize AI spend.