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.

翻译：随着AI驱动工具在软件开发中的日益普及，大语言模型（LLM）已成为代码生成、错误修复和优化等任务的关键工具。ChatGPT、GitHub Copilot和Codeium等工具在解决编程挑战方面提供了宝贵支持，但其实际效能仍有待深入探究。本文对ChatGPT、Codeium和GitHub Copilot进行了比较研究，评估了它们在LeetCode平台上不同难度级别和分类问题中的表现。研究评估了成功率、运行效率、内存使用和错误处理能力等关键指标。GitHub Copilot在简单和中等难度任务中表现优异，而ChatGPT在内存效率和调试方面更为突出。Codeium虽具潜力，但在处理复杂问题时存在困难。尽管各具优势，所有工具在处理高难度问题时均面临挑战。这些发现深化了对各工具能力与局限性的理解，为寻求在编码工作流中优化AI集成的开发者和研究者提供了指导。