Abstract

We develop an economic framework to analyze the optimal pricing and product design of Large Language Models (LLM). Our framework captures several key features of LLMs: variable operational costs of processing input and output tokens; the ability to customize models through fine-tuning; and high-dimensional user heterogeneity in terms of task requirements and error sensitivity. In our model, a monopolistic seller offers multiple versions of LLMs through a menu of products. The optimal pricing structure depends on whether token allocation across tasks is contractible and whether users face scale constraints.

Reference

Dirk Bergemann, Alessandro Bonatti, and Alex Smolin, The Economics of Large Language Models: Token Allocation, Fine-Tuning, and Optimal Pricing, The 26rd ACM Conference on Economics and Computation, July 2025, p. 786.