AI 的「思考档位」不是越贵越好
大模型现在都给你一个「思考强度」选项，调得越高，算得越久、账单越贵。但研究者把四个基准上的模型翻了个底朝天：高思考档位不仅经常在低档能答对的题上翻车，而且在所有档位都能答对的简单题上，最高档比最低档贵最多 42 倍——你多付的钱，大部分时候只是让模型多绕了几圈路，多检查了几遍自己，答案没变。更反直觉的是，它连解题方式都改了：高档位下模型更爱搜索和自查，而不是直接算。所以无脑拉满既不省钱、也不保证答得更好。一个更聪明的做法是：从最低档开始，答错了再升档，这样在所有模型和基准上都能达到或超过固定最高档的效果，而且大多数模型还更省钱。它不是你明天就能抄的作业，但下次你看到「深度思考」按钮时，至少知道那是个价格标签，不是质量保证。
📄 原文摘要(英文)
Generative models increasingly expose a reasoning effort setting that lets users scale inference-time compute, trading higher cost for potentially improved performance. Users must choose an effort level before knowing whether extra reasoning will help, and even a successful run does not reveal whether the effort purchased was necessary. Reasoning effort is therefore a credence good: buyers observe the outcome but cannot assess the marginal value of the computation they paid for. In this work, we measure the counterfactual effect of effort levels on both proprietary and open-weight models on four benchmarks, spanning single-call reasoning and long-horizon agentic tasks. Effort, it turns out, does not consistently preserve tasks that lower effort solves, can overspend, and can change trajectory behavior. First, we find that although higher effort can raise accuracy in aggregate, it frequently fails on tasks that lower effort solved. Second, on the subset of easy tasks that every effort level solves, higher effort is generally more costly, with the highest effort level costing up to 42x as much as the lowest. Lastly, effort changes not only how many tokens are used, but also how agents solve tasks: at higher effort, agents verify their own work more heavily and more often search for solutions than work through problems directly, both of which lengthen trajectories without necessarily altering the outcome. These findings show that naively choosing the highest level is not only cost-inefficient but also offers no guarantee of the best instance-level performance. A simple alternative, starting at the lowest effort and escalating only after a verified failure, matches or improves on fixed top effort across all models and benchmarks at lower cost for most proprietary models.