DeepSeek vs. ChatGPT: Which Performs Better in Python Coding?

This paper conducts a comparative evaluation of two advanced large language models (LLMs) — ChatGPT-۴ and DeepSeek v۳—utilizing ۸۰ algorithmic problems from Code forces categorized into four difficulty levels: Easy (۸۰۰–۱۱۰۰), Intermediate (۱۲۰۰–۱۶۰۰), Advanced (۱۷۰۰–۲۰۰۰), and Expert (۲۱۰۰–۲۴۰۰), focusing on code generation in Python. Standardized prompts and controlled testing conditions enable the assessment of models on accuracy, effi-ciency, and code readability. As the complexity of issues increases, DeepSeek frequently out-performs ChatGPT in both accuracy and efficiency, despite both models excelling in simpler tasks. This, however, results in reduced code clarity and increased memory use. While less pre-cise at elevated levels, ChatGPT produces more concise and idiomatic responses. Both models had limited competence at the expert level; however, DeepSeek-R۱ indicated a slight edge. The study illustrates a trade-off between accuracy and code clarity, so as to inform the selection of LLMs based on task requirements and provide a foundation for future efforts in optimizing code generation models for actual applications.

کلیدواژه ها:

ChatGPT ، DeepSeek ، Coding ، Algorithms ، Python