Exploring Large Language Models for University-Level Mathematics: A Comparative Study

Large language models (LLMs) have shown great potential in solving complex mathematical problems, but their performance in university-level mathematics is still underexplored. This study provides a systematic evaluation of eleven state-of-the-art LLMs on five core undergraduate mathematics courses. An end-to-end automated pipeline is proposed for solution inference and answer evaluation, including a reliable ensemble evaluation scheme using seven reasoning-enabled LLMs as expert evaluators. The experimental results show that reasoning models outperform non-reasoning ones significantly, with DeepSeek-V3.2 and Kimi-K2.5 achieving average scores of 88.24 and 88.99, respectively. Under the conditions of the conducted experiment in this study, DeepSeek-V3.2 achieves the most reasonable accuracy–cost trade-off. This work reveals the strengths and limitations of modern LLMs in advanced mathematical reasoning and provides insights into their application in AI mathematical education.