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Sprint projectOct 26, 2026France

Replacing the Model Does Not Remove the High Price: A Replacement Study of LLM Pricing Agents

Mohamed · Team Cnossos

Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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When two language-model pricing agents have settled on a high price, the cheapest remedy is to replace one of them. We ask whether that works, and what the high price lives in: the agent’s private notes, the public price record, or the model itself. Two copies of Claude Haiku 5.5 price in the Calvano logit duopoly for 100 periods on 24 histories. Each history is forked into matched 20-period continuations: one firm’s agent plays on, or is replaced by a fresh copy of the same model with or without the firm’s records, by another vendor’s model (gpt-oss-120b), or by a no-regret learner, while the other agent plays on uninformed. Analysis and predictions were pre-registered. The incumbents price above the competitive level (profit index Δ = 0.371, 95% interval [0.170, 0.559]). Replacing one by gpt-oss-120b, shown the records, leaves Δ unchanged (paired drop 0.000 [−0.054, 0.060]): the newcomer takes its predecessor’s price, although alone it prices near monopoly. A copy shown nothing does not lower prices: Δ rises by 0.232 [0.030, 0.440], because the fresh agent opens at a default 2.5 and the incumbent follows it upward; the rise is not significant without three histories stalled at 2.5. Forced price cuts are matched more often than forced raises, but full punishment signatures appear in 5 of 24 histories, below our pre-registered cartel criterion. Neither replacement nor memory reset lowered prices within 20 periods: the level a newcomer settles at is the level it is shown.

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  1. This project asks whether a supra-competitive price survives replacing one of two Claude Haiku pricing agents, and what carries it. I appreciate the pre-registration, the synthetic validation of the analysis, and in particular the two-part definition of a cartel, which requires a punishment signature rather than high prices alone. The results are honestly reported: none of the predictions held, the price survives replacement by gpt-oss-120b, and punishment signatures appear in only 5 of 24 histories, so the outcome is parallel supra-competitive pricing rather than a cartel. The observation that a fresh agent opens at a default price of 2.5 is also useful, since it means a “fresh agent” is not a neutral baseline. However, the high price does not really emerge: agents open at 2.5, above the monopoly price, and are still drifting down at the fork, and the gpt-oss newcomer’s first price is close to a copy of its predecessor’s, so survival over 20 periods is nearly mechanical. Moreover, the incumbents’ level is modest and heterogeneous, and the punishment contrast cannot fully separate punishment from simple price-following. In addition, the many reading labels make the results hard to follow. A second model pair, longer continuations, and an other-model newcomer shown nothing would tell us whether the finding is general.

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Cite this project

@misc{mohamed2026replacing,
  title = {{Replacing the Model Does Not Remove the High Price: A Replacement Study of LLM Pricing Agents}},
  author = {Mohamed},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/replacing-the-model-does-not-remove-the-high-price-a-replacement-study-of-llm-pricing-agents-94vg}},
  url = {https://apartresearch.com/sprints/projects/replacing-the-model-does-not-remove-the-high-price-a-replacement-study-of-llm-pricing-agents-94vg}
}

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