@inproceedings{srivastava-etal-2026-llms,
title = "Do {LLM}s Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models",
author = "Srivastava, Gaurav and
Hussain, Aafiya Shamshad and
Srinivasan, Sriram and
Wang, Xuan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1285/",
pages = "25784--25826",
ISBN = "979-8-89176-395-1",
abstract = "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 **$\sim$18$\times$ more tokens** while sometimes achieving **lower accuracy** and exhibit catastrophic collapse when tokens are constrained, dropping by up to **$\sim$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 $\rightarrow$ medium $\rightarrow$ 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."
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<abstract>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 **\sim18\times more tokens** while sometimes achieving **lower accuracy** and exhibit catastrophic collapse when tokens are constrained, dropping by up to **\sim36%**; ****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 \rightarrow medium \rightarrow 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.</abstract>
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%0 Conference Proceedings
%T Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models
%A Srivastava, Gaurav
%A Hussain, Aafiya Shamshad
%A Srinivasan, Sriram
%A Wang, Xuan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F srivastava-etal-2026-llms
%X 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 **\sim18\times more tokens** while sometimes achieving **lower accuracy** and exhibit catastrophic collapse when tokens are constrained, dropping by up to **\sim36%**; ****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 \rightarrow medium \rightarrow 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.
%U https://aclanthology.org/2026.findings-acl.1285/
%P 25784-25826
Markdown (Informal)
[Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models](https://aclanthology.org/2026.findings-acl.1285/) (Srivastava et al., Findings 2026)
ACL