Compare Coefficients of Different Regression Models
Weights across different types of models are not always comparable, so
I think that it would make more sense to do this kind of comparison not across different types of model but within a single type of model varying:


the hyper-parameters (if any),
the set of instances (e.g. selecting different subsets randomly),
the set of features.


I'd recommend in particular varying the instances: if the weight of a feature tends to change a lot depending on the training set it's a sign of overfitting.

If the goal is to compare the importance of some specific features for different models, it's probably more reliable to directly evaluate how the model performs with/without the feature(s).