Selecting a predicted protein model for your research or analysis depends on your specific needs and the available resources. Predicted protein models can be generated using various computational methods, and the choice of model can impact the accuracy and relevance of your study. Here are some common approaches to consider when selecting a predicted protein model:

You can attempt to resolve this issue by performing a minimization or simulation of your protein for a few nanoseconds. For a large disorder region, you can use Ab Initio method.

Did you 3D-align the different models generated by the different servers? Do you get any convergences for the overall fold of the molecule? Most server give you some indicators of the reliability of the predictions specific to the method used and the different regions of the model. If each server gives you a widely different answer for the overall fold, I would not trust any of them. On the other hand, if you see a common core structure, and the main difference lies e.g. in the different orientation of large loops or the relative orientations of two or more domains connected by flexible linkers, you may be dealing with a protein what contains highly flexible or intrinsically disordered regions. You might find closely related experimental structures for the individual folded domain in the PDB, while the flexible regions had to be excised to be able to crystallise the protein. The UniProt record of you protein is a good first stop to learn about what is known about important interaction sites, active sites and post-translational modifications, with links to the relevant publications and databases. Be aware of the fact that many real-life proteins contain large flexible regions. These flexible regions are normally not represented in the collection of known experimental structures, and consequently, are also missing from the datasets used to train AI structure prediction systems.