One key difference is that Julia's multiple dispatch is not tied to a single class hierarchy. It works based on a flexible type system, which means you can define new types and dispatch functions on them without modifying existing definitions. This makes it easier to extend and customize existing code in a modular way.

In Julia, multiple dispatch allows functions to be specialized based on the types of all their arguments, not just the first one. This leads to more efficient and flexible code as it can intelligently choose the most specific method to execute based on the input types. In Python, for example, dispatch is based only on the type of the first argument, which can limit the expressiveness and performance of the code.

Another advantage of multiple dispatch in Julia is that it allows for parametric dispatch, where methods can be specialized based on the values of their arguments. This enables concise and efficient implementations of algorithms that depend on specific parameter values.

Multiple dispatch in Julia brings the benefits of both dynamic and static dispatch. It combines the runtime flexibility of dynamic dispatch with the performance optimizations of static dispatch by specializing functions based on the actual argument values observed during execution.

In contrast to C++, Julia provides a more dynamic dispatch mechanism. C++ relies on static dispatch, which means that the decision about which function to call is made at compile-time. In Julia, dispatch is performed dynamically at runtime, allowing for more flexibility and adaptability.