[2603.23971] The Price Reversal Phenomenon: When Cheaper Reasoning Models Cost More

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Computer Science > Computation and Language

arXiv:2603.23971 (cs)

[Submitted on 25 Mar 2026 (v1), last revised 28 May 2026 (this version, v2)]

Title:The Price Reversal Phenomenon: When Cheaper Reasoning Models Cost More

Authors:Lingjiao Chen, Chi Zhang, Yeye He, Ion Stoica, Matei Zaharia, James Zou<br>View a PDF of the paper titled The Price Reversal Phenomenon: When Cheaper Reasoning Models Cost More, by Lingjiao Chen and Chi Zhang and Yeye He and Ion Stoica and Matei Zaharia and James Zou

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Abstract:Developers and consumers increasingly choose reasoning models (RMs) based on their listed API prices. However, how accurately do these prices reflect actual inference costs? We conduct the first systematic study of this question, evaluating 8 frontier RMs across 12 diverse tasks covering competition math, science QA, code generation, and multi-domain agents. We uncover the pricing reversal phenomenon: in 32% of model-pair comparisons, the model with a lower listed price actually incurs a higher total cost, with reversal magnitude reaching up to 28x. For example, Gemini 3 Flash's listed price is 80% cheaper than GPT-5.4's, yet its actual cost across all tasks is 38% higher. We build a formal cost attribution framework based on Shapley value, and leverage it to trace the dominating contributors to vast heterogeneity in thinking token consumption and number of interaction turns: on the same query, one model may use 900% more thinking tokens than another, or 10x more turns of environment interactions. We further show that per-query cost prediction is fundamentally difficult: repeated runs of the same query yield thinking token variation up to 9.7x, establishing an irreducible noise floor for any predictor. Thus, we propose cost distribution prediction as an open challenge. Our findings demonstrate that listed API pricing is an unreliable proxy for actual cost, calling for cost-aware model selection and transparent per-request cost monitoring.

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Multiagent Systems (cs.MA)

Cite as:<br>arXiv:2603.23971 [cs.CL]

(or<br>arXiv:2603.23971v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.23971

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arXiv-issued DOI via DataCite

Submission history<br>From: Lingjiao Chen [view email]<br>[v1]<br>Wed, 25 Mar 2026 06:07:39 UTC (700 KB)

[v2]<br>Thu, 28 May 2026 01:52:03 UTC (20,514 KB)

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