  Recent advancements in language models have showcased human-comparable
performance in academic entrance exams. However, existing studies often
overlook questions that require the integration of visual comprehension, thus
compromising the full spectrum and complexity inherent in real-world scenarios.
To address this gap, we present a comprehensive framework to evaluate language
models on entrance exams, which incorporates both textual and visual elements.
We evaluate the two most recent editions of Exame Nacional do Ensino M\'edio
(ENEM), the main standardized entrance examination adopted by Brazilian
universities. Our study not only reaffirms the capabilities of GPT-4 as the
state of the art for handling complex multidisciplinary questions, but also
pioneers in offering a realistic assessment of multimodal language models on
Portuguese examinations. One of the highlights is that text captions
transcribing visual content outperform the direct use of images, suggesting
that the vision model has room for improvement. Yet, despite improvements
afforded by images or captions, mathematical questions remain a challenge for
these state-of-the-art models. The code and data used on experiments are
available at https://github.com/piresramon/gpt-4-enem.
