AI 中文总结
该研究提出TEI，一种同行基准复合指标，通过纳入组织AI使用数据计算三类指标并聚合，生成0-100分等结果，以透明方式基准AI令牌效率并识别支出优化机会。
AI 中文摘要
随着人工智能（AI）在科技巨头、原生AI初创企业及非技术组织中的应用加速，一个看似简单的问题却难以回答：相关支出是否高效？AI消费按令牌定价，成本因令牌类型（输入、输出、推理）和模型类型而异，使用量从简单查询的数百令牌到多步骤智能体任务的超100万令牌不等。这种差异使得在无标准化框架的情况下，组织内部及跨组织的成本比较变得困难。我们提出令牌效率指数（TEI），这是一种同行基准复合指标，可将令牌支出效率浓缩为0-100的单一分数。TEI不依赖底层提供商，可纳入组织的AI使用数据，并计算三个具有方向感知的指标：缓存命中率、缓存摊销率和 premium 模型份额。这些指标被归一到同一尺度，并通过等权重复合及基于优势的（BoD）数据包络分析（DEA）模型进行聚合，针对稀疏数据采用稳健的order-m扩展。最终输出标题分数、同行百分位数及具有估算节省量的前沿差距建议。TEI基于复合指标和DEA文献的成熟方法，提供了一种透明、可解释的方法来基准AI令牌效率并识别优化AI支出的机会。
英文摘要
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.
Comments11 pages, 6 figures
