In this paper, we present the masala-chai team’s participation in the LegalLens 2024 shared task and detail our approach to predicting legal entities and performing natural language inference (NLI) in the legal domain. We experimented with various transformer-based models, including BERT, RoBERTa, Llama 3.1, and GPT-4o. Our results show that state-of-the-art models like GPT-4o underperformed in NER and NLI tasks, even when using advanced techniques such as bootstrapping and prompt optimization. The best performance in NER (accuracy: 0.806, F1 macro: 0.701) was achieved with a fine-tuned RoBERTa model, while the highest NLI results (accuracy: 0.825, F1 macro: 0.833) came from a fine-tuned Llama 3.1 8B model. Notably, RoBERTa, despite having significantly fewer parameters than Llama 3.1 8B, delivered comparable results. We discuss key findings and insights from our experiments and provide our results and code for reproducibility and further analysis at https://github.com/rosequ/masala-chai