This is the kind of AI stuff that really annoys me. Looking at one of the mutation examples I didn't see anything that wouldn't normally be tested by a typical mutation tool. You took a simple, idempotent process and you got an llm to do it slower, less accurately, and using more resources.
If you wanted to marry the two in a new and possibly useful fashion I would say use an llm to analyze the results of a standard mutation test and give guidance on what issues should be acted upon first. An off-by-one calculation could mean somebody loses a million dollars or it could mean a button is grayed out. Standard mutation tools don't give you that context.