Spam email is one of the main vectors of cyberattacks containing scams and spreading malware. Spam emails can contain malicious and external links and attachments with hidden malicious code. Hence, cybersecurity experts seek to detect this type of email to provide earlier and more detailed warnings for organizations and users. This work is based on a binary classification system (with and without malware) and evaluates models that have achieved high performance in other natural language applications, such as fastText, BERT, RoBERTa, DistilBERT, XLM-RoBERTa, and Large Language Models such as LLaMA and Mistral. Using the Spam Email Malware Detection (SEMD-600) dataset, we compare these models regarding precision, recall, F1 score, accuracy, and runtime. DistilBERT emerges as the most suitable option, achieving a recall of 0.792 and a runtime of 1.612 ms per email.