OptimalThinkingBench: Evaluating Over and Underthinking in LLMs

Thinking LLMs solve complex tasks at the expense of increased compute and overthinking on simpler problems, while non-thinking LLMs are faster but underthink on harder reasoning problems. This has led to separate thinking and non-thinking variants, leaving model selection to end users. We introduce OptimalThinkingBench, a unified benchmark that jointly evaluates overthinking and underthinking while encouraging development of optimally-thinking models that balance performance and efficiency. Our benchmark comprises two sub- benchmarks: OverthinkingBench, featuring simple queries in 72 domains, and UnderthinkingBench, containing 11 challenging reasoning tasks. Using novel thinking-adjusted accuracy metrics, we perform extensive evaluation of 33 different thinking and non-thinking models and show that no model is able to optimally think on our benchmark. Thinking models often overthink for hundreds of tokens on the simplest user queries without improving performance. In contrast, large non-thinking models ``underthink'', often falling short of much smaller thinking models. We further explore several methods to encourage optimal thinking, but find that these approaches often improve on one sub-benchmark at the expense of the other, highlighting the need for better unified and optimal models in the future.