Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
DeepSeek vs. ChatGPT: Which Performs Better in Python Coding? |
| Συγγραφείς: |
Abdalla, Rania A. M.1 r.alkhateeb@ptuk.edu.ps |
| Πηγή: |
Journal of Information Technology Management (JITM). 2026, Vol. 18 Issue 2, p1-27. 27p. |
| Θεματικοί όροι: |
*Algorithms, ChatGPT, Python programming language, Code generators, Language models |
| Περίληψη: |
This paper conducts a comparative evaluation of two advanced large language models (LLMs) — ChatGPT-4 and DeepSeek v3—utilizing 80 algorithmic problems from Code forces categorized into four difficulty levels: Easy (800–1100), Intermediate (1200–1600), Advanced (1700–2000), and Expert (2100–2400), focusing on code generation in Python. Standardized prompts and controlled testing conditions enable the assessment of models on accuracy, efficiency, 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 precise at elevated levels, ChatGPT produces more concise and idiomatic responses. Both models had limited competence at the expert level; however, DeepSeek-R1 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. [ABSTRACT FROM AUTHOR] |
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