Multimodal large language models have made substantial progress in medical visual understanding, but temporal reasoning and knowledge-guided reasoning over real surgical videos remain underexplored. Existing surgical benchmarks mainly focus on static frames, single procedures, or simple questions, which limits their ability to capture the complexity of real clinical workflows. To address this gap, we introduce UniSurgBench, a unified benchmark for open-ended surgical video reasoning, comprising 2,302 question-answer samples collected from real surgical videos spanning 45 procedures and 29 anatomical structures. Each sample is annotated with temporally grounded evidence and structured reasoning steps. We also propose SurgExpEval, a two-stage evaluation protocol that extracts structured reasoning components from free-form model outputs and jointly assesses answer accuracy, reasoning quality, and temporal grounding. Evaluations with 23 proprietary, open-source, and medical-related MLLMs show that MLLMs can produce clinically reasonable and causally coherent responses. However, they often fail to associate these responses with fine-grained visual evidence, highlighting evidence-level visual grounding as a key bottleneck for reliable surgical intelligence.