Dynamic perioperative risk prediction requires models to anticipate evolving physiological deterioration under strict prediction-time information boundaries. We introduce PERIBENCH, a protocol-aware benchmark built from high-frequency intraoperative signals. PERIBENCH defines 18 clinically motivated tasks spanning future risk classification, persistent deterioration prediction, continuous vital-sign regression, and case-level prognosis, and evaluates 37 systems across numerical predictors, deep sequential models, time-series foundation models, and LLM-oriented language interfaces. Rather than using a single undifferentiated leaderboard, PERIBENCH separates supervised or adapted prediction, direct pretrained transfer, and prompt-based language-interface inference into protocol-compatible pools. Experiments show that model sophistication does not translate monotonically into better perioperative prediction: tree-based references remain strong on many classification endpoints, deep sequential models are competitive on selected physiological tasks, direct foundation-model transfer is heterogeneous, and language interfaces capture useful classification signals but remain less reliable for regression, calibration, alert burden, and abnormal-region prediction. PERIBENCH provides a structured benchmark for studying dynamic perioperative prediction from numerical models to language interfaces under shared task semantics and explicit protocol boundaries. Code is available at https://github.com/Kdyyh/PERIBENCH.