Intent classification and sentiment analysis stand as pivotal tasks in natural language processing, with applications ranging from virtual assistants to customer service. The advent of transformer-based models has significantly enhanced the performance of various NLP tasks, with encoder-only architectures gaining prominence for their effectiveness. More recently, there has been a surge in the development of larger and more powerful decoder-only models, traditionally employed for text generation tasks. This paper aims to answer the question of whether the colossal scale of newer decoder-only language models is essential for real-world applications by comparing their performance to the well established encoder-only models, in the domains of intent classification and sentiment analysis. Our results shows that for such natural language understanding tasks, encoder-only models in general provide better performance than decoder-only models, at a fraction of the computational demands.