With the rise of Generative AI (GenAI) models in recent years, it is necessary to understand how they performed compared with other Deep Learning techniques, across tasks and across different languages. In this study, we benchmark ChatGPT-4 and XML-RoBERTa, a multilingual transformer-based model, as part of the Multilingual Binary and Multiclass Hope Speech Detection within the PolyHope-M 2025 competition. Furthermore, we explored prompting techniques and data augmentation to determine which approach yields the best performance. In our experiments, XML-RoBERTa frequently outperformed ChatGPT-4. It also attained F1 scores of 0.86 for English, 0.83 for Spanish, 0.86 for German, and 0.94 for Urdu in Task 1, while achieving 0.73 for English, 0.70 for Spanish, 0.69 for German, and 0.60 for Urdu in Task 2.