Bergemann, Dirk, Bonatti, Alessandro and Smolin, Alexey (2025) The Economics of Large Language Models: Token Allocation, Fine-Tuning, and Optimal Pricing. TSE Working Paper, n. 25-1670, Toulouse

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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. Users with similar aggregate value-scale characteristics
choose similar levels of fine-tuning and token consumption. The optimal mechanism
can be implemented through menus of two-part tariffs, with higher markups for more
intensive users. Our results rationalize observed industry practices such as tiered pricing
based on model customization and usage levels.