Large Language Models (LLMs) have taken the world by storm, demonstrating impressive abilities in various tasks, from writing poems to generating code. But how well do these powerful AIs handle the realm of mathematics, which demands precise logic and step-by-step reasoning? Researchers tackled this question by rigorously benchmarking several popular LLMs, including both open-source models like LLaMA and closed-source giants like GPT, across various math reasoning datasets, from grade-school problems to complex college-level challenges. They also explored different prompting methods, like Chain-of-Thought prompting, to see if they could enhance the LLMs' mathematical prowess. The results reveal a fascinating interplay between model size, prompting strategy, and the inherent difficulty of the math problems. Larger models, unsurprisingly, tended to fare better, with GPT-4 and LLaMA 3-70B showcasing the strongest performance. However, the choice of prompting method also played a crucial role, particularly for smaller models. For LLaMA, a technique called Auto CoT provided the best balance between performance and efficiency. While Zero-Shot CoT proved effective for GPT-3.5 on many tasks, suggesting concise prompts can be surprisingly powerful. The research also highlighted the challenges LLMs face with higher-level math. Even the most capable models struggled with college-level datasets like AQuA, hinting that true mathematical reasoning is still a frontier for AI. This detailed benchmarking study illuminates both the strengths and limitations of current LLMs in mathematical reasoning. While they can efficiently tackle many everyday math problems, more advanced mathematical skills remain a significant hurdle. The study's insights will undoubtedly guide future research as developers push the boundaries of what AI can achieve in the domain of numbers and logic.