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This paper presents a comparative analysis of OpenAI's GPT-4 and its optimized variant, GPT-4o, focusing on their architectural differences, performance, and real-world applications. GPT-4, built upon the Transformer architecture, has set new standards in natural language processing (NLP) with its capacity to generate coherent and contextually relevant text across a wide range of tasks. However, its computational demands, requiring substantial hardware resources, make it less accessible for smaller organizations and real-time applications. In contrast, GPT-4o addresses these challenges by incorporating optimizations such as model compression, parameter pruning, and memory-efficient computation, allowing it to deliver similar performance with significantly lower computational requirements. This paper examines the trade-offs between raw performance and computational efficiency, evaluating both models on standard NLP benchmarks and across diverse sectors such as healthcare, education, and customer service. Our analysis aims to provide insights into the practical deployment of these models, particularly in resource-constrained environments.
Siddiky et al. (Mon,) studied this question.