Benchmarking ChatGPT, Codeium, and GitHub Copilot: A Comparative Study of AI-Driven Programming and Debugging Assistants

TL;DR

This study compares ChatGPT, Codeium, and GitHub Copilot in AI-driven programming, evaluating their effectiveness on coding challenges to understand their strengths and limitations.

Contribution

It provides a comprehensive benchmarking of major AI coding assistants across multiple performance metrics and difficulty levels, highlighting their respective capabilities.

Findings

GitHub Copilot performs best on easier tasks

ChatGPT excels in debugging and memory efficiency

Codeium struggles with complex problems

Abstract

With the increasing adoption of AI-driven tools in software development, large language models (LLMs) have become essential for tasks like code generation, bug fixing, and optimization. Tools like ChatGPT, GitHub Copilot, and Codeium provide valuable assistance in solving programming challenges, yet their effectiveness remains underexplored. This paper presents a comparative study of ChatGPT, Codeium, and GitHub Copilot, evaluating their performance on LeetCode problems across varying difficulty levels and categories. Key metrics such as success rates, runtime efficiency, memory usage, and error-handling capabilities are assessed. GitHub Copilot showed superior performance on easier and medium tasks, while ChatGPT excelled in memory efficiency and debugging. Codeium, though promising, struggled with more complex problems. Despite their strengths, all tools faced challenges in handling harder problems. These insights provide a deeper understanding of each tool's capabilities and limitations, offering guidance for developers and researchers seeking to optimize AI integration in coding workflows.