Say I have a bag of dice one of each of the usual D&D denominations (d4, d6, d8, d10, d12, d20). I draw one at random, ask the models for predictions, and roll it. Model A ignores the information about which one I drew, and predicts a correct distribution of rolls (12.9% chance of rolling a 6). Model B correctly processes the information about which one I drew, and predicts a correct distribution given that information (I drew the d6 so 17% chance of rolling a 6). Both models give correct results overall, but Model B has higher probabilities on average, and I would say it is a better model.

A model should be judged both on how accurately it characterizes its uncertainty and how much evidence it's able to successfully make use of.

You can do better if you have foreknowledge or retroactive foreknowledge of the outcome of the die roll, which is the obvious suggestion of jtolmar's comment. If I know the recorded outcomes of a sequence of die rolls, I can have models that predict those outcomes to any accuracy I want. But they're not doing it by measuring the uncertainty involved in prospectively rolling the die.