Skip to main content
numpy-ts is 1.25x faster than NumPy on average across 7,159 benchmarks (small, medium, and large arrays) and runs natively in JavaScript + WASM with zero dependencies.
All benchmarks compare computation time between JS (numpy-ts) and Python (NumPy with OpenBLAS), measured on each side respectively. See methodology for details.

​
Performance by Category

numpy-ts outperforms NumPy in most categories. NumPy leads in bitwise, trig, and math — all active areas of improvement.
Performance by category: numpy-ts vs NumPy
See the full breakdown of category results on the numpy-ts vs. NumPy page.

​
Performance by Data Type

Smaller data types see the biggest gains — numpy-ts’s SIMD kernels process more elements per instruction for int8, uint8, and float16. Even float64 (NumPy’s home turf) is on par.
Performance by data type: numpy-ts vs NumPy
See the full breakdown of dtype results on the numpy-ts vs. NumPy page.

​
Performance by Array Size

numpy-ts is as fast or faster than NumPy at every tested array scale — from small (100-element) arrays where low overhead matters, to large (10K-element) arrays where SIMD throughput dominates.
Performance by array size: numpy-ts vs NumPy
See the full breakdown of array size results on the size scaling page.

​
All Benchmarks

vs. NumPy (Native)

How does numpy-ts compare to NumPy running natively in Python with OpenBLAS?

vs. NumPy (Pyodide)

How does numpy-ts compare to NumPy running in WebAssembly via Pyodide?

Performance Scaling by Size

How does numpy-ts performance scale across small, medium, and large array sizes?

Node.js, Deno & Bun

How does numpy-ts perform across different JavaScript runtimes?