COMPARATIVE SEARCH ANALYSIS OF GENERATIVE AI MODELS A Case Study of ChatGPT, Gemini and Perplexity BY ABROZIEKEYA BERNARD OGHENEOVO PSC1712789 DEPARTMENT OF COMPUTER SCIENCE, FACULTY OF PHYSICAL SCIENCES, UNIVERSITY OF BENIN, BENIN CITY, EDO STATE, NIGERI
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Abstract
This paper presents a comprehensive comparative search analysis of three prominent generative AI models: ChatGPT, Gemini, and Perplexity. By focusing on their architecture, performance, training processes, and real-world applications, we provide a detailed case study that highlights the strengths and limitations of each model in various natural language processing (NLP) tasks. The analysis covers aspects such as model scalability, accuracy, response time, and adaptability across different domains. Through systematic benchmarking and evaluation of these models on both factual and creative prompts, we explore their potential to revolutionize industries such as education, customer service, and content generation. This study also aims to inform future developments in generative AI by identifying gaps and opportunities for improvement in model design and training methodologies.
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