对比ChatGPT与DeepSeek解决编程题表现，发现后者在中等难度上明显落后。
A Showdown of ChatGPT vs DeepSeek in Solving Programming Tasks
- 用Codeforces 29道题测试模型解题能力，分难易三档
- 中等难度下ChatGPT成功率达54.5%，DeepSeek仅18.1%
- 适合关注AI编程工具实际性能的开发者和研究者
大型语言模型的进展催生了人工智能辅助编程工具的竞争格局。本研究评估了两个领先模型：ChatGPT 03-mini 和 DeepSeek-R1 在解决 Codeforces 上的竞赛编程任务中的表现。基于29个不同难度（简单、中等、困难）的编程任务，从通过率、内存效率和运行时间三个维度进行评估。结果显示，两模型在简单任务上表现相近；但在中等难度任务上，ChatGPT 成功率达到54.5%，远超 DeepSeek-R1 的18.1%。两者在困难任务上均表现不佳，凸显当前大模型在处理高复杂度编程问题时仍面临挑战。研究揭示了两类模型在能力与计算资源上的差异，为开发者与研究人员优化AI编程工具提供了关键参考。
原文摘要 · Abstract (English)
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.
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