The Economics of Large Language Models: Token Allocation, Fine-Tuning, and Optimal Pricing

Dirk Bergemann, Alessandro Bonatti and Alex Smolin

No 25-1670, TSE Working Papers from Toulouse School of Economics (TSE)

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.

Keywords: Large Language Models; Optimal Pricing; Menu Pricing; Fine-Tuning (search for similar items in EconPapers)
JEL-codes: D47 D82 D83 (search for similar items in EconPapers)
Date: 2025-10
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:130997

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