AI 的“思考档位”越贵越好？不一定
大模型现在都给你一个“思考力度”的档位，调得越高，它算得越久、你付得越多。直觉是：多花点钱，答案总该更靠谱吧？这篇研究把四个模型、四类任务翻了个底朝天，结论是：不一定。高力度不仅经常在低力度能答对的题上翻车，而且在大家都答对的简单题上，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，比如反复检查自己、多搜几次方案，最后答案没变。研究者给了一个更聪明的玩法：从最低档开始，答错了再升档，这样在所有模型和任务上都不输给一上来就拉满，而且大多数时候更省钱。它不是你明天必须用上的，但下次你看到“深度思考”选项时，可以少一点迷信。
📄 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.