ChatGPT与DeepSeek在解决编程任务中的对决
A Showdown of ChatGPT vs DeepSeek in Solving Programming Tasks
AI总结：
本文评估了ChatGPT和DeepSeek在解决编程任务中的性能，发现ChatGPT在中等难度任务中表现更优，而两者在困难任务上均面临挑战。
摘要：
大语言模型（LLMs）的发展为AI辅助编程工具创造了竞争环境。本研究评估了ChatGPT 03-mini和DeepSeek-R1在Codeforces上解决编程任务的能力。使用三个难度级别的29个编程任务，我们通过接受的解决方案、内存效率和运行时间性能评估了两种模型的表现。我们的结果表明，尽管两者在简单任务上表现相似，但ChatGPT在中等难度任务中表现更优，成功率为54.5%，而DeepSeek为18.1%。两者在困难任务上均面临挑战，突显了LLMs在处理高度复杂编程问题方面的持续挑战。这些发现突显了两种模型在能力和计算能力上的关键差异，为开发者和研究人员改进AI驱动的编程工具提供了有价值的见解。
英文摘要：
The advancement of large language models (LLMs) has created a competitive landscape for AI-assisted programming tools. This study evaluates two leading models: ChatGPT 03-mini and DeepSeek-R1 on their ability to solve competitive programming tasks from Codeforces. Using 29 programming tasks of three levels of easy, medium, and hard difficulty, we assessed the outcome of both models by their accepted solutions, memory efficiency, and runtime performance. Our results indicate that while both models perform similarly on easy tasks, ChatGPT outperforms DeepSeek-R1 on medium-difficulty tasks, achieving a 54.5% success rate compared to DeepSeek 18.1%. Both models struggled with hard tasks, thus highlighting some ongoing challenges LLMs face in handling highly complex programming problems. These findings highlight key differences in both model capabilities and their computational power, offering valuable insights for developers and researchers working to advance AI-driven programming tools.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
