This paper presents a comprehensive performance evaluation of Large Language Models (LLMs) in solving programming challenges from Leetcode, a widely used platform for algorithm practice and technical interviews. We began by crawling the Leetcode website to collect a diverse set of problems encompassing various difficulty levels and topics. Using this dataset, we generated solutions with multiple LLMs, including GPT-4 and GPT-3.5-turbo (ChatGPT-turbo). The generated solutions were systematically evaluated for correctness and efficiency. We employed the pass@k metric to assess the success rates within a given number of attempts and analyzed the runtime performance of the solutions. Our results highlight the strengths and limitations of current LLMs [10] in code generation and problem-solving tasks, providing insights into their potential applications and areas for improvement in automated programming assistance.
View on arXiv@article{wang2025_2502.15770,
title={ Performance Review on LLM for solving leetcode problems },
author={ Lun Wang and Chuanqi Shi and Shaoshui Du and Yiyi Tao and Yixian Shen and Hang Zheng and Yanxin Shen and Xinyu Qiu },
journal={arXiv preprint arXiv:2502.15770},
year={ 2025 }
}