Open-ended tasks in Natural Language processing are characterized by the absence of a fixed set of outputs or short, predetermined responses. Typical examples include open-domain question answering and summarization. The automatic evaluation of these tasks is challenging, and existing metrics suffer from important limitations. Thus, many works have already been proposed to evaluate the performance of such metrics. However, the great majority of these works were conducted in English, leaving a gap for analyses in other languages, such as Portuguese. This work investigates the performance of some of the most popular automated intrinsic evaluation metrics for open-ended tasks by analyzing their correlation with human judgment. Using both the Summarization and Question Answering tasks, this study compares traditional n-gram-based metrics with metrics based on contextual embeddings generated by Pre-trained Language Models (PLMs), performing all analyses using models and datasets available for Portuguese. Results indicate that while PLM-based metrics generally show slightly higher correlation with human perception, their performance is highly reliant on the quality of the models used. Still, none of the evaluated metrics displayed a strong correlation with human judgments, suggesting the need for more robust metrics.