Some of the key features that distinguish Julia from other programming languages are its high performance, dynamic typing, multiple dispatch, and optional typing. Julia is designed to be fast, with a just-in-time (JIT) compiler that can generate highly optimized machine code. Dynamic typing allows for flexible and expressive code, while multiple dispatch enables efficient code organization and specialization. Additionally, Julia provides optional typing, allowing developers to specify types for variables and function arguments when desired.

One of the standout features of Julia is its ability to seamlessly call and interface with code written in other languages such as C, Python, and R. This makes Julia a versatile language for scientific computing and data analysis, as it allows users to leverage the extensive libraries and tools available in these ecosystems. Furthermore, Julia's support for distributed computing enables parallelization and easy scaling of computations across multiple cores or even clusters of machines.

Julia sets itself apart by combining the best aspects of dynamic languages like Python with the speed and efficiency of statically-typed languages like C++. It features a sophisticated type inference system that allows for high-level programming without sacrificing performance. Moreover, Julia provides powerful metaprogramming capabilities, enabling users to generate code dynamically at runtime.