Beyond Benchmarks: The Economics of AI Inference

Boqin Zhuang; Jiacheng Qiao; Mingqian Liu; Mingxing Yu; Ping Hong; Rui Li; Xiaoxia Song; Xiangjun Xu; Xu Chen; Yaoyao Ma; Yujie Gao

arXiv · 2510.26136

Beyond Benchmarks: The Economics of AI Inference

Boqin Zhuang·Jiacheng Qiao·Mingqian Liu·Mingxing Yu·Ping Hong·Rui Li·Xiaoxia Song·Xiangjun Xu·Xu Chen·Yaoyao Ma·Yujie Gao

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Abstract

The inference cost of Large Language Models (LLMs) has become a critical factor in determining their commercial viability and widespread adoption. This paper introduces a quantitative ``economics of inference'' framework, treating the LLM inference process as a compute-driven intelligent production activity. We analyze its marginal cost, economies of scale, and quality of output under various performance configurations. Based on empirical data from WiNEval-3.0, we construct the first ``LLM Inference Production Frontier,'' revealing three principles: diminishing marginal cost, diminishing returns to scale, and an optimal cost-effectiveness zone. This paper not only provides an economic basis for model deployment decisions but also lays an empirical foundation for the future market-based pricing and optimization of AI inference resources.

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Boqin Zhuang, Jiacheng Qiao, Mingqian Liu, Mingxing Yu, Ping Hong, Rui Li, Xiaoxia Song, Xiangjun Xu, Xu Chen, Yaoyao Ma, Yujie Gao. 2025-10-30. Beyond Benchmarks: The Economics of AI Inference. https://arxiv.org/abs/2510.26136

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