Mojo vs. NumPy vs. Vyper vs. Zig Comparison Chart

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.

Zig is a general-purpose programming language and toolchain for maintaining robust, optimal and reusable software. Focus on debugging your application rather than debugging your programming language knowledge. A fresh approach to metaprogramming based on compile-time code execution and lazy evaluation. No hidden control flow. No hidden memory allocations. No preprocessor, no macros. Call any function at compile-time. Manipulate types as values without runtime overhead. Comptime emulates the target architecture. Use Zig as a zero-dependency, drop-in C/C++ compiler that supports cross-compilation out-of-the-box. Leverage zig build to create a consistent development environment across all platforms. Add a Zig compilation unit to C/C++ projects; cross-language LTO is enabled by default.