All the other answers are true. But there is one thing I didn't see people saying. Thanks to the existence of macros, you can create->compile code in runtime. This allows for faster solving of some problems which are too dynamic, thanks to the fast compile times of Julia.
This might sound counterintuitive given that latency is a normal problem mentioned everywhere else about Julia. But, if you think about it, Julia compiled to native code a plot library from scratch in 15- seconds every time you imported it (before Julia 1.9 where native caching of code was introduced, and latency was cut down significantly).
This makes that problems where you would like to (for example) generate polynomials in runtime and evaluate then a billion times each, Julia can generate efficient code for ever polynomial, compile it and run it fast those billion times. C/C++/Fortran would have needed to write a (really fast) genetic function to evaluate polynomials, but this would have always (TM) been less efficient than code generated and optimised for them.
Edit: typos and added some remarks lacking originally
This might sound counterintuitive given that latency is a normal problem mentioned everywhere else about Julia. But, if you think about it, Julia compiled to native code a plot library from scratch in 15- seconds every time you imported it (before Julia 1.9 where native caching of code was introduced, and latency was cut down significantly).
This makes that problems where you would like to (for example) generate polynomials in runtime and evaluate then a billion times each, Julia can generate efficient code for ever polynomial, compile it and run it fast those billion times. C/C++/Fortran would have needed to write a (really fast) genetic function to evaluate polynomials, but this would have always (TM) been less efficient than code generated and optimised for them.
Edit: typos and added some remarks lacking originally