Computational Arbitrage in AI Model Markets
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 verifiabl...
| Authors | Ricardo Olmedo, Bernhard Schölkopf, Moritz Hardt |
|---|---|
| Primary category | cs.AI |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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