Analyzing Prominent LLMs: An Empirical Study of Performance and Complexity in Solving LeetCode Problems

The rapid advancement of Generative AIs (GenAIs), particularly Large Language Models (LLMs), has transformed software engineering by automating tasks such as code generation, testing, and debugging. As these models become increasingly integrated into development workflows, evaluating their performance systematically is crucial for optimizing their effectiveness in real-world applications. This study aims to benchmark six prominent LLMs - ChatGPT, Copilot, Gemini, Claude, Perplexity, and DeepSeek - on algorithm and data structure problems from LeetCode, assessing their strengths and limitations in solving programming challenges. The study evaluates LLM performance on 150 LeetCode problems, generating solutions in Java and Python. Performance metrics include execution time, memory usage, and computational complexity (time and space). A joint analysis ranks the models based on multiple performance factors. The evaluation reveals variations in LLM efficiency, with some models consistently outperforming others across different difficulty levels. Copilot, DeepSeek, and Perplexity demonstrate strong performance, while Gemini struggles with harder problems. Differences in execution time and memory usage are also noted across programming languages. The findings contribute to a deeper understanding of LLM capabilities in code generation and provide insights to help developers make informed decisions when selecting LLMs based on problem complexity and programming language.