This article explores the use of quasirecurrent neural networks (QRNNs) in selected natural language processing (NLP) tasks—specifically encoding, classification, regression, and named entity recognition. Our hypothesis suggests that QRNNs, as a simpler recurrent architecture, might offer advantages over long short-term memory (LSTM) networks, such as faster inference times and better utilisation of computational resources. To test our hypothesis, we conducted an empirical investigation that implemented both QRNN and LSTM architectures, and compared their performance in terms of quality and speed in selected NLP tasks. Although QRNNs performed better than LSTM networks, the improvement was less substantial than in established architectures. QRNNs deliver various benefits, such as rapid training and fine-tuning, lower memory and data needs, and the prevention of overfitting on small datasets through proper regularisation techniques. We believe that QRNNs represent a compelling and unique concept that has yet to be fully embraced as a mainstream NLP architecture.