In natural language processing (NLP), transformer-based models have grown in popularity over the past few years. In light of the fact that these models have demonstrated promising outcomes across a number of metrics for example. For the purpose of comparing and contrasting the GPT-4 and BERT language models in a variety of settings, this research makes use of a wide variety of natural language processing applications within their respective categories. This research makes use of a battery of classification tasks in order to investigate the architecture of the GPT-4 and BERT language models as well as their performance in a variety of different environments.