Large Language Models have shown strong scientific reasoning ability, but their performance on materials science problems remains less studied. To fill this gap, we introduce MatSciBench, a comprehensive college-level benchmark comprising 1340 problems that span the essential subdisciplines of materials science. MatSciBench features a structured and fine-grained taxonomy that categorizes materials science questions into 6 primary fields and 31 subfields, together with a three-tier difficulty classification based on the reasoning length needed to solve each problem. MatSciBench includes detailed reference solutions for 946 questions, supports process-level error analysis, and contains 315 questions with images for evaluating multimodal reasoning. We evaluate leading thinking and non-thinking LLMs on MatSciBench, and further test three reasoning methods for non-thinking models: basic chain-of-thought prompting, tool augmentation, and self-correction. The results show that current models still face clear limits in college-level materials science reasoning. DeepSeek-R1 achieves the highest score on text-only questions at 75.22% accuracy, and GPT-5 performs the best on questions with images at 53.02%. Our analysis shows that tool augmentation improves many non-thinking models in a token-efficient way, while self-correction often fails to provide reliable gains and can revise correct answers into incorrect ones. We further analyze performance across difficulty levels, reasoning efficiency, multimodal reasoning, and failure patterns, and find that current models are mainly limited by domain knowledge gaps, calculation errors, problem comprehension failures, and difficulty in extracting precise information from scientific figures. Overall, MatSciBench provides a clear testbed for measuring current LLM limitations and guiding future work on scientific reasoning in materials science.