The Economics of Large Language Models: Token Allocation, Fine Tuning, and Optimal Pricing
Dirk Bergemann,
Alessandro Bonatti and
Alex Smolin
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Alessandro Bonatti: Massachusetts Institute of Technology
No 2425, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University
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.
Pages: 43 pages
Date: 2025-02-11
New Economics Papers: this item is included in nep-ain, nep-cmp, 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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