This master’s thesis evaluated automatic post-editing using large language models on neural machine translation outputs. The evaluation was designed to understand the results provided by different APE settings and in response to the research tasks: 1/ Evaluate and compare different APE settings (NMT+LLM) based on the quality improvements (BLEU) for machine translation; 2/ Evaluate and compare different APE settings’ effort and performance in post-editing, with a novel performance evaluation approach that assesses APE efficiency in achieving Task 1. The APE settings were from a combination of two LLMs and three NMTs. Both automatic metrics and human expert evaluation were adopted. We also proposed dividing the delta BLEU by the TER to evaluate a given APE setting’s efficiency. Although the results were consistent in both test and control groups, this metric still needs a more solid literature review, particularly in understanding the relationship between the APE’s effort and its energy consumption.