Menu Pricing of Large Language Models

2025/02/11 by Dirk Bergemann, Bergemann, Dirk, Alessandro Bonatti +3 · 2 citations
Computer Science · Economics, Econometrics and Finance · #Economic Policies and Impacts #FOS: Economics and business #Text Readability and Simplification #Theoretical Economics (econ.TH)

paper · pdf · doi:10.48550/arxiv.2502.07736

openalex publication_date 2025/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

We develop a framework for the optimal pricing and product design of LLMs in which a provider sells menus of token budgets to users who differ in their valuations across a continuum of tasks. Under a homogeneous production technology, we show that users' high-dimensional type profiles are summarized by a scalar index, reducing the seller's problem to one-dimensional screening. The optimal mechanism takes the form of committed-spend contracts: buyers pay for a budget that they allocate across token classes priced at marginal cost. We extend the analysis to environments with multiple differentiated models and to competition between a proprietary leader and an open-source fringe, showing that competitive pressure reshapes both the intensive and extensive margins of compute provision. Each element of our theory (token-budget menus, maximum- and minimum-spend plans, multi-model versioning, and linear API pricing) has a direct counterpart in the observed pricing practices of providers such as Anthropic, OpenAI, and GitHub.

Cited by

• Monopolistic Data Dumping
• Auditing Pay-Per-Token in Large Language Models

Related

• Inference economics of language models
• Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives
• Tiered Super-Moore's Law: Price Evolution, Production Frontiers, and Market Competition in Large Language Model Inference Services
• Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models
• Cost Transparency of Enterprise AI Adoption