⚡ lightning-levenshtein

Fast Levenshtein distance in pure JavaScript.

lightning-levenshtein uses tiny-string specializations, precompiled 32-bit kernels, fixed-width Myers variants, and a generalized large-input fallback to deliver high throughput across the full input range.

Installation

npm install lightning-levenshtein

npm package contents

The published package includes two production builds:

dist/
  lightning-levenshtein.min.js
  lightning-levenshtein-v2.min.js

Default API

The default package entrypoint exposes the stable core API:

import { distance, distanceMax, closest } from "lightning-levenshtein";

This resolves to dist/lightning-levenshtein.min.js.

V2 API

A second public subpath export is also available:

import { levenshteinLightning } from "lightning-levenshtein/v2";

This resolves to dist/lightning-levenshtein-v2.min.js.

The v2 build is a separate optimized runtime that uses more aggressive length-based dispatch, tiny-string fast paths, precompiled 32-bit kernels, fixed-width Myers variants, and a generalized large-input fallback. It is not just a renamed copy of the default package entrypoint.

Which one should users pick?

Use the default package entrypoint if you want the stable general-purpose npm API:

import { distance, distanceMax, closest } from "lightning-levenshtein";

Use the v2 subpath if you specifically want the specialized levenshteinLightning runtime:

import { levenshteinLightning } from "lightning-levenshtein/v2";

Current package exports

The package currently exposes both entrypoints through exports:

{
  "exports": {
    ".": "./dist/lightning-levenshtein.min.js",
    "./v2": "./dist/lightning-levenshtein-v2.min.js"
  }
}

What it does

• Specialized kernels for very short strings
• Precompiled bit-parallel dispatch for short inputs
• Fixed-width Myers variants for medium inputs
• Generalized Myers fallback for large inputs
• Zero runtime dependencies
• Works in Node.js and browsers

Strategy

The runtime selects the cheapest correct kernel for the current input size.

• 1–32 chars: precompiled bit-parallel kernels
• 33–64 chars: fixed-width Myers specialization
• 65–96 chars: fixed-width Myers specialization
• 97–128 chars: fixed-width Myers specialization
• 129–224 chars: generalized macro-block Myers dispatch
• 225–256 chars: fixed-width Myers specialization
• 257–512 chars: generalized macro-block Myers dispatch
• 513+ chars: large-input generalized Myers dispatch

This keeps tiny inputs fast without sacrificing larger-input performance.

Benchmark

The benchmark harness generates the same string pairs for every library at each tested length and seed.

Benchmarks were run in Node.js:

• Node version: v24.11.0

Methodology

• 500 random equal-length string pairs per test size
• 3 seeds: 1337, 7331, 20250321
• 500 ms measurement window per seed
• 3 warm-up rounds before timing
• alphabet: A-Z, a-z, 0-9
• reported table values: mean ops/ms across 3 seeds

Mean ops/sec

| Test Target | N=1 | N=2 | N=4 | N=8 | N=16 | N=32 | N=64 | N=128 | N=256 | N=512 | N=1024 | |---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| | lightning-levenshtein-v2 | 180629 | 91386 | 50257 | 32369 | 11949 | 6514 | 1805 | 581.9 | 158.0 | 35.92 | 9.327 | | lightning-levenshtein-v1 | 94625 | 61719 | 40905 | 25507 | 8577 | 4938 | 1224 | 466.0 | 134.5 | 35.25 | 9.117 | | fastest-levenshtein | 100634 | 74359 | 43909 | 23150 | 8307 | 4240 | 1087 | 290.2 | 78.41 | 19.55 | 5.092 | | js-levenshtein | 115773 | 87076 | 23668 | 11343 | 3373 | 924.0 | 241.7 | 61.35 | 15.92 | 3.985 | 1.006 | | leven | 82490 | 41685 | 21482 | 8188 | 1785 | 420.2 | 114.2 | 29.90 | 7.607 | 1.913 | 0.481 | | levenshtein-edit-distance | 114674 | 55327 | 24957 | 8463 | 1792 | 396.6 | 106.6 | 27.38 | 6.992 | 1.754 | 0.438 |

Relative throughput vs fastest-levenshtein

This chart normalizes fastest-levenshtein to 100% at each string length and shows every other library relative to that baseline.

Use this graph when you want an apples-to-apples comparison against the package most people already know. Values above 100% mean faster than fastest-levenshtein; values below 100% mean slower.

Throughput across input sizes

Mean ops/sec shown on a log-scaled Y axis across the full tested range.

Relative throughput vs fastest-levenshtein

This chart normalizes fastest-levenshtein to 100% at each string length and shows lightning-levenshtein relative to that fixed baseline.

We use fastest-levenshtein as the canonical public reference point because it is a widely recognized JavaScript Levenshtein implementation. Values above 100% mean faster than fastest-levenshtein; values below 100% mean slower.

Rank by input length

This chart shows where each library ranks at each tested string length.

This is useful because raw throughput can be noisy to read at a glance, while rank makes the ordering obvious. If a library is consistently ranked first across the range, you can see that immediately without squinting at the absolute numbers.

Results

• lightning-levenshtein is the fastest library in this benchmark set at every tested length.
• It leads at N=1, N=2, N=4, N=8, N=16, N=32, N=64, N=128, N=256, N=512, and N=1024.
• At N=1024, mean throughput is 9.36 ops/ms versus 4.98 ops/ms for fastest-levenshtein.
• At N=32, mean throughput is 6568 ops/ms versus 4240 ops/ms for fastest-levenshtein.
• At N=8, mean throughput is 33126 ops/ms versus 23288 ops/ms for fastest-levenshtein.

Reproducing the benchmark

npm run bench:packages
npm run bench:packages:table
npm run bench:packages:chart

Generated files are written to bench/packages/.

Project layout

bench/bolt/
  lev-dispatch.js
  levenshtein-lightning-v2.js
  levenshtein_Direct_Matrix.js
  myers32_v4.js
  myers_64.js
  myers_96.js
  myers_128.js
  myers_256.js
  myers_x.js
  myers_x64.js

bench/packages/
  run-bench.js
  render-readme-table.js
  render-readme-line-chart.js
  render-readme-rank-chart.js
  render-relative-bar-chart.js
  render-readme-relative-fastest-chart.js
  results.json

License

See LICENSE.md.