lev

lev is an extremely fast Python library for the Levenshtein distance and similarity ratio, written in Rust. 🦀

Installation

uv add lev-rs

or if you prefer slow:

pip install lev-rs

Usage

import lev

lev.distance("kitten", "sitting")   # 3
lev.distance("résumé", "resume")    # 2
lev.distance("日本語", "日本")       # 1

lev.ratio("kitten", "sitting")      # 0.769...
lev.ratio("", "")                   # 1.0

For more details on the API see the API Reference.

Benchmarks

lev is benchmarked against the fastest Python Levenshtein libraries: rapidfuzz, editdistance, and edlib. We excluded slower implementations like pylev and python-Levenshtein.

Benchmarks were run on an Apple Mac Mini M2 Pro (macOS 26.2) using Python 3.13. Each string pair is exactly 100 characters long. Results represent the total wall time for 1,000 repetitions using Python's timeit. To reproduce, run uv run python scripts/benchmark.py.

ASCII

lev is significantly faster than the other libraries on 100-character ASCII strings.

ASCII benchmark – dark

Other Encodings

lev maintains its lead across all four CPython string-encoding kinds.

Latin-1

Latin-1 benchmark – dark

CJK

CJK benchmark – dark

Emoji

Emoji benchmark – dark

Contact

To get in contact, please open an issue or contact me via github@markusbilz.com.

Project links

Data verified by PyPI on Jul 9, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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Latest release

1 maintainer

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Avatar for KarelZe from gravatar.com KarelZe

Credits

Author: Markus Bilz

GitHub Statistics

Data verified by PyPI on Jul 9, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

Requires

Python >=3.10

Provides Extra

benchmark dev docs

Tags

distance levenshtein strings

Classifiers

Report project as malware

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This release

0.2.3 This release

21 files

0.2.2

16 files

0.2.1

16 files

0.2.0

16 files

0.1.0

16 files

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