Unverified50% confidenceOpinionExact time
Reasons why thinking models perform worse than base models on LEAPS include exhausting the generation length budget or over-simplifying problems on complex tasks.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/20/2026
First Seen
Valid until: 1/18/2027
Sources
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Related Claims
VerifiedLonger inference time does not always yield better results; models may fall into the trap of overthinking, and what truly determines output quality is the model's foundational capabilities and degree of alignment.79% similarUnverifiedEffort is an amplifier, while model tier is the capability ceiling; adding thinking budget to a smaller model cannot bridge the capability gap inherent to the model itself.76% similarUnverifiedIn multi-step agent tasks, typically only a small number of steps require top-tier model reasoning; the majority of intermediate steps are glue logic, and using high-end models uniformly across all steps wastes budget.76% similarUnverifiedWhen a model gives answers of vastly different quality for semantically equivalent but differently phrased questions, it indicates that the model has overfitted to surface-level wording rather than truly mastering the underlying task.76% similarUnverified轻量模型在高难度任务上的失败往往是结构性的，切换模型比优化提示词更务实76% similar
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