It is uniformly more efficient than the maximum likelihood estimator across the entire parameter space.

It achieves a lower variance than the Cramér-Rao lower bound at specific points, but fails to be uniformly better.

It is always biased and therefore cannot be compared to the Cramér-Rao lower bound.

It satisfies the conditions of the Le Cam convolution theorem for all values of $\theta$.