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

Dirk Bergemann, Alessandro Bonatti and Alex Smolin

Papers from arXiv.org

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.

Date: 2025-02
New Economics Papers: this item is included in nep-com, nep-des and nep-mic
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Working Paper: The Economics of Large Language Models: Token Allocation, Fine Tuning, and Optimal Pricing (2025)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2502.07736

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