Matlab/Octave is great for numerical programs that perform within an order of magnitude of Fortran. If some things aren't fast enough, you can rewrite them in C or Fortran without too much trouble. If you're doing anything other than numerical computing, it's awful, and you should use a different language.
(Source: I did a PhD using a mixture of Octave for numerical stuff, Perl for text-processing and automation, and C++ for the parts that were too slow. Choose the right tool for the job.)
Modern Fortran is better all around. The compiler will check usage based on interface. It has a working and supported module system (unlike C++). A couple of openmp pragmas will parallelize it. Multidimensional dense arrays are first class objects. The compiler can emit code with array bounds checking. Keyword and optional arguments. Standardized C FFI. f2py inter-op with Python/numpy.
Most people encounter large FORTRAN IV or FORTRAN 77 heirloom codes, and assume that's what Fortran is like in 2025.
Why does this matter in the least? Like you must understand that this is a library call right? Like just put `import numpy as np` in your PYTHONSTARTUP and it's the exact same UX in python.
(Source: I did a PhD using a mixture of Octave for numerical stuff, Perl for text-processing and automation, and C++ for the parts that were too slow. Choose the right tool for the job.)