Menu Pricing of Large Language Models
Dirk Bergemann, Alessandro Bonatti, Alex Smolin

TL;DR
This paper presents a theoretical framework for optimal pricing of large language models, focusing on menu-based token budgets and competitive market dynamics, aligning with real-world provider practices.
Contribution
It introduces a novel one-dimensional screening model for LLM pricing, including multi-model and competitive extensions, connecting theory with observed industry pricing strategies.
Findings
Optimal committed-spend contracts for token budgets
Market competition influences pricing and model differentiation
Pricing strategies align with industry practices of providers like OpenAI
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,…
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Taxonomy
TopicsEconomic Policies and Impacts · Text Readability and Simplification
