Semiparamatric Bayesian models have become increasingly popular over the past few decades. As compared to their parametric counterparts, the semiparametric models allow for a greater flexibility in capturing the parameter uncertainty. Dirichletprocess mixed models form a particular class of Bayesian semiparametric models by assuming a random mixing distribution, taken to be a realization from a Dirichlet process, for the mixture. In this research, we show that while hierarchical DP models may provide flexibility in model fit, they may not perform uniformly better in other aspects as compared to the parametric models.