Classroom Final Exam: An Instructor-Tested Reasoning Benchmark
Authors: name: Chongyang Gao, name: Diji Yang, name: Shuyan Zhou, name: Xichen Yan, name: Luchuan Song, name: Shuo Li, name: Kezhen Chen
Organization: Analogy AI, Inc.

We introduce  (Classroom Final Exam), a multimodal benchmark for evaluating the reasoning capabilities of large language models across more than 20 STEM domains.  is curated from repeatedly used, authentic university homework and exam problems, together with reference solutions provided by course instructors.  presents a significant challenge even for frontier models: the newly released Gemini-3.1-pro-preview achieves an overall accuracy of 59.69\%, while the second-best model, Gemini-3-flash-preview, reaches 55.46\%, leaving considerable room for improvement. Beyond leaderboard results, we perform a diagnostic analysis by decomposing reference solutions into reasoning flows. We find that although frontier models can often answer intermediate sub-questions correctly, they struggle to reliably derive and maintain correct intermediate states throughout multi-step solutions. We further observe that model-generated solutions typically have more reasoning steps than those provided by the instructor, indicating suboptimal step efficiency and a higher risk of error accumulation. The data and code are available at https://github.com/Analogy-AI/CFE_Bench.