cs.AIMar 23, 2026
SaveComputational Arbitrage in AI Model Markets
Organizations: Max Planck Institute for Intelligent Systems, Tübingen
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.
Figures & tables
Figure 1: Consider a model market with three providers: GPT-5 mini, DeepSeek v3.2, and an arbitrageur. Consumers demand a target level of performance on SWE-bench–type tasks; for example, a 75% SWE-bench solve rate. Through repeated sampling, GPT-5 mini and DeepSeek each achieve a 75% SWE-bench solve rate at costs of 120, respectively. The arbitrageur instead sources generations by first querying GPT-5 mini (at up to 80. This cost advantage creates a profit opportunity: the arbitrageur can resell its sourced generations at markups of up to 50% while still undercutting the market.
Figure 2: Inference cost for GPT-5 mini and DeepSeek v3.2 to reach different SWE-bench performance levels, with inference budgets scaled through repeated sampling (up to 0.08 to GPT-5 mini and, if it fails, spend the remaining $0.92 on DeepSeek. The arbitrage policy (red) achieves solve rates above 68% at a lower cost than either GPT-5 mini or DeepSeek. This cost advantage enables the arbitrageur to profit by reselling its generations close to market price (purple).
Figure 3: Two arbitrageurs deploy the same arbitrage policy but compete on price. They take turns updating their prices to undercut each other. Earlier turns are plotted with greater transparency. Left: Competition between arbitrageurs drives market prices downward. In equilibrium, the market price equals the arbitrageurs’ buy price. Right: While arbitrage is initially highly profitable, profit opportunities eventually vanish.
Figure 4: Revenue split across model providers for different levels of performance. Left: In the absence of arbitrageurs, the market is segmented by performance, with a single model dominating each segment. Middle: Arbitrageurs eliminate this segmentation, allowing both models to earn revenue across a much broader range of levels of performance. Right: Arbitrageurs reduce providers’ marginal revenue, with the lost surplus transferred to arbitrageur profits or passed on to consumers as lower market prices.
Figure 5: Profit margin across different search budgets when fitting the arbitrage policy. The solid line represents mean profitability, whereas the shaded area indicates the 95% confidence interval, computed by bootstrapping over the samples acquired within the search budget. Left: When fitting a fixed query distribution, small search budgets (e.g., $10) consistently yield profitable arbitrage policies. Middle and right: We fit the arbitrageur either on software issues from the Django library or on issues from other repositories, and evaluate the resulting arbitrage policy on the held-out data. We find that, on expectation, the arbitrageur remains profitable under such distribution shifts in the query distribution.
Figure 6: Model market with six providers of varying sizes. Left: Cost for each model to achieve different levels of SWE-bench performance. Middle: Revenue split across providers. There is little market segmentation, with up to four providers sharing revenue at a given performance level. Some models, such as Qwen Coder 480B and Claude Sonnet 4.5, are not competitive. Right: Compared with the earlier two-model market (GPT-5 mini and DeepSeek), the six-model market yields more, and more profitable, arbitrage opportunities.
Figure 7: We fine-tune Qwen 3 1.7B using data generated by Qwen Coder 30B. We distill for up to 400k examples (5.4B tokens). Left : Models distilled on more data Pareto-dominate in terms of pass@ . Middle : Models distilled on more data attain higher levels of performance at lower cost. Right: When paired with Qwen Coder 480B, models distilled on more data create increasingly more profitable arbitrage opportunities.
Figure 8: We train mini-coder 4B with data generated by Qwen Coder 30B. Left: mini-coder outperforms Qwen Coder at inference budgets up to $0.02 per issue. Middle: When paired with GPT-5 mini, mini-coder leads to lower market prices (i.e., arbitrage buy costs) than Qwen Coder. Right: Upon entering a market consisting of GPT-5 mini and Qwen Coder, mini-coder cannibalizes nearly all of Qwen Coder’s revenue.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
| Model | Input ($) | Output ($) | Cache Reduction |
| Claude Sonnet 4.5 | 3.00 | 15.00 | 90% |
| GPT-5 mini | 0.25 | 2.00 | 90% |
| Qwen 3 Coder 480B | 0.25 | 1.00 | 90% |
| DeepSeek v3.2 Reasoner | 0.28 | 0.42 | 90% |
| Qwen 3 Coder 30B | 0.07 | 0.27 | 90% |
| mini-coder 4B | 0.02 | 0.07 | 90% |
Table 1: Model pricing per 1M tokens.
Figure 9: Left: price correction by enforcing early-stopping after the first bash command is generated. Right : price correction by applying the adverstised 90% caching discount to GPT-5 mini.
Figure 10: Left : Inference cost for each Kimina Prover model to reach various MiniF2F solve rates. We plot in red the best inference cost achievable by an arbitrageur that distributes inference compute across the Kimina models. Right : Arbitrageurs can create opportunities for profit, achieving over 60% profit margin.
Figure 11: MiniF2F theorem proving. Revenue split across model providers for different levels of performance. Left: In the absence of arbitrageurs, the market is segmented by performance, with a single provider dominating each segment. Middle: Arbitrageurs eliminate this segmentation, allowing providers to earn revenue across broader ranges of performance. Right: Arbitrageurs reduce providers’ marginal revenue.
Figure 12: Scaling experiments for Lean theorem proving. We fine-tune Qwen 3 1.7B using synthetic training trajectories from Kimina Prover 1.7B. We distill for up to 200k examples (5B tokens). Left : Increased distillation improves test-time scaling. Models distilled on more data Pareto-dominate in terms of pass@ . Middle : We explicitly consider inference budget in FLOPs (i.e., pass@FLOPs). We similarly find that increased distillation monotonically improves pass@FLOPs. Right: Arbitrage profit when pairing each of the distilled models with Kimina Prover 72B. Models distilled on more teacher data create increasingly more profitable arbitrage opportunities.