Menu Pricing of Large Language Models

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, and linear API pricing) has a direct counterpart in the observed pricing practices of providers such as Anthropic, OpenAI, and GitHub.