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Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources such as text speech images and videos. Despite its success in language understanding it is critical to evaluate the performance of downstream tasks for better human-centric applications. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing spanning from visual affective tasks and reasoning tasks. The results show that\gpt has high accuracy in facial action unit recognition and micro-expression detection while its general facial expression recognition performance is not accurate. We also highlight the challenges of achieving fine-grained micro-expression recognition and the potential for further study and demonstrate the versatility and potential of\gpt for handling advanced tasks in emotion recognition and related fields by integrating with task-related agents for more complex tasks such as heart rate estimation through signal processing. In conclusion this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing.