Abstract
Pretrained Large Language Models (LLMs) have achieved remarkable success across diverse domains, with education and research emerging as particularly impactful areas. Among current state-of-the-art LLMs, ChatGPT and DeepSeek exhibit strong capabilities in mathematics, science, medicine, literature, and programming. In this study, we present a comprehensive evaluation of these two LLMs through background technology analysis, empirical experiments, and a real-world user survey. The evaluation explores trade-offs among model accuracy, computational efficiency, and user experience in educational and research affairs. We benchmarked these LLMs performance in text generation, programming, and specialized problem-solving. Experimental results show that ChatGPT excels in general language understanding and text generation, while DeepSeek demonstrates superior performance in programming tasks due to its efficiency- focused design. Moreover, both models deliver medically accurate diagnostic outputs and effectively solve complex mathematical problems. Complementing these quantitative findings, a survey of students, educators, and researchers highlights the practical benefits and limitations of these models, offering deeper insights into their role in advancing education and research.
Abstract (translated)
预训练大型语言模型（LLMs）在各个领域取得了显著的成功，特别是在教育和研究方面尤为突出。当前最先进的LLM中，ChatGPT和DeepSeek展示了在数学、科学、医学、文学和编程等多个领域的强大能力。本研究通过技术背景分析、实证实验以及真实用户调查对这两种LLM进行了全面评估。此次评估探索了模型准确性、计算效率与用户体验之间的权衡问题，并针对教育和科研事务中的文本生成、编程及专业问题解决等方面，对这些LLM的性能进行了基准测试。 实验结果显示，在通用语言理解和文本生成方面，ChatGPT表现出色；而在编程任务中，DeepSeek因其专注于效率的设计而展现出优异的表现。此外，这两种模型都能提供准确的医学诊断结果，并能有效地解决复杂的数学问题。为了补充这些定量研究发现，我们对学生们、教育工作者和研究人员进行了调查，探讨了这些模型的实际益处与局限性，为了解它们在促进教育与科研发展方面的作用提供了更深入的见解。
URL
https://arxiv.org/abs/2512.08057
