Compare Hidden Markov Model's sample with ground truth data
Compute the likelihood of the observed data, for each model.  Then higher the likelihood, the better the fit.  The likelihood is just the probability that the model assigns to the observed data, which for HMMs can be computed using dynamic programming.
Be prepared that the more complex the model, the higher the likelihood will be, but that doesn't necessarily mean the model is &quot;better&quot; -- you run the risk of overfitting.  Larger HMMs may fit the training data better, but if the HMM is too large, it might perform poorly on new data because of overfitting.