Benchmarking Large Language Models: A Comparative Study of DeepSeek and ChatGPT Across Diverse Domains

Abstract

The growing integration of Large Language Models (LLMs) like ChatGPT and DeepSeek across education and professional domains highlights the need to understand their domain-specific performance and response behavior. This study evaluates 400 prompts and 800 responses across four core fields—Business, Healthcare, Mathematics, and Neuroscience—using both simple and Retrieval-Augmented Generation (RAG) prompts. Responses were rated on Accuracy, Relevance, Complexity, and Runtime using a 5-point Likert scale. Correlation analysis using Pearson coefficients revealed key relationships between evaluation metrics, showing that Relevance and Accuracy are strongly aligned in high-performing domains, while Complexity often correlates with longer response times. Our findings indicate that ChatGPT excels in producing accurate and efficient outputs, while DeepSeek demonstrates strength in generating contextually rich and complex responses, particularly with RAG prompts. This research provides practical insights into how prompt design impacts model behavior and offers actionable recommendations for educators, developers, and learners to optimize prompt strategies when using generative Artificial Intelligence (AI) tools. The study underscores the role of prompt engineering in enhancing LLM performance and supports the development of tailored, domain-aware AI applications in education and beyond.

Ähnliche Arbeiten