Large language models (LLMs) achieve impressive performance on complex mathematical benchmarks yet sometimes fail on basic math reasoning while generating unnecessarily verbose responses. In this paper, we present **LLMThinkBench**, a systematic benchmark and comprehensive empirical study to evaluate the efficiency of reasoning in LLMs, focusing on the fundamental tradeoff between accuracy and overthinking. **First,** we formalize the *accuracy-verbosity tradeoff*. **Second,** we introduce the *Overthinking Score*, a harmonic-mean metric combining accuracy and token-efficiency for holistic model evaluation. **Third,** we establish an evaluation protocol with dynamically-generated data across **14** basic math tasks. **Fourth,** we conduct a large-scale empirical study evaluating **53** LLMs, including reasoning and quantized variants across different reasoning budgets. **Fifth,** we release **LLMThinkBench** as an open-source Python package and public leaderboard for reproducibility. Our findings reveal: ****1)**** model performance on complex benchmarks does not translate directly to basic math reasoning; ****2)**** reasoning models generate **∼18× more tokens** while sometimes achieving **lower accuracy** and exhibit catastrophic collapse when tokens are constrained, dropping by up to **∼36%**; ****3)**** the accuracy-verbosity relationship is non-monotonic with extended reasoning budgets yielding diminishing returns (GPT-5/o-series models show zero accuracy gain from **low → medium → high** reasoning effort). *Our findings challenge the assumption that longer reasoning in LLMs necessarily improves mathematical reasoning.* Our public leaderboard is available at https://ctrl-gaurav.github.io/LLMThinkBench/. Our open-source Python package is available at https://pypi.org/project/llmthinkbench/, and the codebase can be found at https://github.com/ctrl-gaurav/LLMThinkBench for easy and reproducible evaluation.