Dynamic Event Tree (DET) analysis, one of the main dynamic Probabilistic Safety Assessment methods, provides a framework to capture the effect of dynamics on the risk estimate. Depending on how Continuous Stochastic Variables (CSVs) are treated, DETs can be classified into discretization—or sampling-based methods. The accuracy of the estimate and required computational resources depend on the method chosen as well as the nature of the problem. CSVs also include variable initial conditions, some of which significantly impact accident evolution. This work compares alternative DET methods in terms of numerical accuracy and computational resources for a case study of a chemical batch reac- tor problem, which is sensitive to accident dynamics as well as variable initial conditions. The reference solution is a computationally intensive analog Monte Carlo simulation. The results show that the DET methods fairly match reference results with significantly less computation.