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The Token Efficiency Index: A Peer-Benchmarked Composite Indicator for AI Token Efficiency — arXiv:2608.07304

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.07304
License (as archived): CC0-1.0
Archived: 2026-10-19T09:48:54.018Z
SHA-256 of the archived text: 6d04f704176862b7c7d45779ef45920887b0a90c8cf8d14b37678b1e2b814713
Stable URL of this page: https://metraipolis.com/documento/dc_cf9bfbbbdbbb

How to cite

The Token Efficiency Index: A Peer-Benchmarked Composite Indicator for AI Token Efficiency — arXiv:2608.07304 — arXiv (en), https://arxiv.org/abs/2608.07304 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cf9bfbbbdbbb (Metraipolis archive dc_cf9bfbbbdbbb, sha256 6d04f704176862b7c7d45779ef45920887b0a90c8cf8d14b37678b1e2b814713)

BibTeX

@misc{dccf9bfbbbdbbb,
  title = {The Token Efficiency Index: A Peer-Benchmarked Composite Indicator for AI Token Efficiency — arXiv:2608.07304},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.07304},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cf9bfbbbdbbb; sha256 6d04f704176862b7c7d45779ef45920887b0a90c8cf8d14b37678b1e2b814713},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.07304. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-08-10 in cs.PF).
Categorie arXiv: Performance (cs.PF) ; Computational Engineering, Finance, and Science (cs.CE).
Pagina del lavoro: https://arxiv.org/abs/2608.07304 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T09:48:04.736Z dalla pagina https://arxiv.org/catchup/cs.PF/2026-08-10?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).