Computational Arbitrage in AI Model Markets

2026-03-23

AI Summary

This work addresses the significant disparity in price and performance among AI models in emerging model markets, where users seek cost-effective, verifiable high-quality inference services but lack mechanisms to efficiently aggregate resources across multiple models. We present the first systematic study of computational arbitrage in AI model markets and propose a dynamic arbitrage framework that allocates inference budgets across models by integrating robust arbitrage strategies with model distillation. Evaluated on verifiable benchmarks such as SWE-bench, our approach delivers reliable, low-cost inference services, achieving up to a 40% net profit margin on GitHub issue repair tasks. Furthermore, competition among multiple arbitrageurs not only substantially reduces user costs but also fosters market consolidation and enhances participation opportunities for smaller model providers.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Consider a market of competing model providers selling query access to models with varying costs and capabilities. Customers submit problem instances and are willing to pay up to a budget for a verifiable solution. An arbitrageur efficiently allocates inference budget across providers to undercut the market, thus creating a competitive offering with no model-development risk. In this work, we initiate the study of arbitrage in AI model markets, empirically demonstrating the viability of arbitrage and illustrating its economic consequences. We conduct an in-depth case study of SWE-bench GitHub issue resolution using two representative models, GPT-5 mini and DeepSeek v3.2. In this verifiable domain, simple arbitrage strategies generate net profit margins of up to 40%. Robust arbitrage strategies that generalize across different domains remain profitable. Distillation further creates strong arbitrage opportunities, potentially at the expense of the teacher model's revenue. Multiple competing arbitrageurs drive down consumer prices, reducing the marginal revenue of model providers. At the same time, arbitrage reduces market segmentation and facilitates market entry for smaller model providers by enabling earlier revenue capture. Our results suggest that arbitrage can be a powerful force in AI model markets with implications for model development, distillation, and deployment.

Problem

• computational arbitrage
• AI model markets
• market competition
• economic consequences
• model distillation

Innovation

• computational arbitrage
• AI model markets
• inference budget allocation
• model distillation
• verifiable AI services