[2603.22404] Computational Arbitrage in AI Model Markets

Computer Science > Artificial Intelligence arXiv:2603.22404 (cs) [Submitted on 23 Mar 2026] Title:Computational Arbitrage in AI Model Markets Authors:Ricardo Olmedo, Bernhard Schölkopf, Moritz Hardt View a PDF of the paper titled Computational Arbitrage in AI Model Markets, by Ricardo Olmedo and 2 other authors View PDF HTML (experimental) 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 segmenta...