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: MIT Sloan - Sloan School of Management - MIT - Massachusetts Institute of Technology

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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.

Keywords: Fine-Tuning; Menu Pricing; Optimal Pricing; Large Language Models (search for similar items in EconPapers)
Date: 2025-10-10
Note: View the original document on HAL open archive server: https://hal.science/hal-05308118v1
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Related works:
Working Paper: The Economics of Large Language Models: Token Allocation, Fine-Tuning, and Optimal Pricing (2025)
Working Paper: The Economics of Large Language Models: Token Allocation, Fine Tuning, and Optimal Pricing (2025)
Working Paper: The Economics of Large Language Models: Token Allocation, Fine-Tuning, and Optimal Pricing (2025)
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