Performance Comparison of Over 40 Programming Languages on a 1‑Billion‑Iteration Nested Loop

The article presents a performance benchmark of many programming languages, focusing on the execution time of a 1 billion‑iteration nested loop and a Fibonacci calculation, using a MacBook Pro (M3, 16 GB RAM) as the test platform.

Results show that compiled languages (C, Rust, Java) achieve the best performance, completing the loop in about 0.5 seconds, while interpreted languages (Python, Ruby, PHP) are significantly slower, with Python taking 74.42 seconds in the initial test.

For the Fibonacci benchmark, C and Rust remain the fastest (0.40 s and 0.41 s respectively), whereas Python and R are the slowest (29.00 s and 68.96 s).

Additional tests using hyperfine confirm similar trends: Zig, Rust and C again lead with ~0.5 s, Python 3.13 improves to 31.589 s, and R remains the worst at 72.643 s.

The environment details include the compiler and interpreter versions for each language (e.g., Apple clang 16.0.0, Rust cargo 1.82.0, Python 3.9.6/3.13, Ruby 3.3.5, PHP 8.3.13, etc.).

Instructions to reproduce the benchmark are provided: clone the repository git clone https://github.com/bddicken/languages , navigate to the loops directory, compile with ../compile.sh , run with ../run.sh , and clean up with ../clean.sh . Sample output shows timing results for Zig, C, Rust, and other languages.

The article concludes that for compute‑intensive tasks, compiled languages such as C, Rust, and Java are preferable, while languages like Python, PHP and Ruby are better suited for scenarios prioritizing development speed over raw performance.

At the end, a promotional note invites readers to scan a QR code to receive free Python learning resources, including e‑books, tutorials, and source code.