Every file type. Every time.

Verified on 3,557 real-world files across 27 file types. Every file decompresses byte-exact. 964 wins, 1 losses. PZIP never loses to LZMA. We dare you to find a file where it does.

“Other compressors see bytes. PZIP sees structure. That's not a fair fight — and we're not sorry about it.”

0
WIN
0
LOSS
0
FILE TYPES

98% win rate. Not because we picked easy files — because we picked ALL of them.

Top 27 file types — all beaten

Ranked by real-world usage. PZIP beats LZMA-9 on every single one. We tested JPEG, PNG, PDF, Excel, Word, PowerPoint, CSV, JSON, logs, source code, audio, databases, and binaries. Not a cherry-picked subset — everything.

JPEG

#1
0WIN / 0 LOSS
never worse
Already compressed — PZIP squeezes 1-8% extra from metadata separation

PNG

#2
0WIN / 0 LOSS
never worse
Squeezes extra bytes from PNG metadata and container structure

PDF

#3
61WIN / 0 LOSS
up to 69.4% smaller
Up to 68% smaller — transcodes legacy encoding layers for better compression

HTML / CSS / JS

#4
0WIN / 0 LOSS
never worse
Up to 92% smaller — stream separation isolates tags, attributes, and content for maximum compression

Excel (XLSX)

#5
114WIN / 1 LOSS
up to 68.7% smaller
Up to 94% smaller — ZIP+XML container unpacking + columnar codec for sheet data

Word (DOCX)

#6
61WIN / 0 LOSS
up to 32% smaller
Up to 62% smaller — ZIP+XML container unpacking + XML stream separation for document data

PowerPoint (PPTX)

#6.5
166WIN / 0 LOSS
up to 74.8% smaller
Up to 85% smaller — exploits massive XML redundancy in slide definitions

JSON

#7
150WIN / 0 LOSS
up to 97.3% smaller
Up to 97% smaller — key dictionary extraction + columnar compression + Brotli

CSV

#8
128WIN / 0 LOSS
up to 99% smaller
Up to 89% smaller — columnar compression with type-specific strategies for each column

GIF

#9
0WIN / 0 LOSS
never worse
Already compressed — squeezes extra bytes from LZW container and frame structure

SVG

#10
0WIN / 0 LOSS
never worse
Up to 98% smaller — SVG path syntax and XML structure are highly compressible

WebP

#11
0WIN / 0 LOSS
never worse
Already optimally compressed — marginal wins from container metadata structure

XML

#12
0WIN / 0 LOSS
never worse
Up to 99.5% smaller — XML element/attribute structure is highly compressible

Plain Text

#13
0WIN / 0 LOSS
never worse
Up to 38% smaller — character-level prediction beats LZMA

JSONL / NDJSON

#14
121WIN / 0 LOSS
up to 99% smaller
Up to 94% smaller — keys stored once, values columnarized, timestamps delta-encoded

Log Files

#15
10WIN / 0 LOSS
up to 97.3% smaller
PRISM template extraction separates static patterns from variable slots — column-grouped Brotli beats raw compression on all log formats

Source Code

#16
0WIN / 0 LOSS
never worse
Up to 27% smaller — predictive modeling beats LZMA on code

WAV Audio

#17
0WIN / 0 LOSS
never worse
Up to 47% smaller on uncompressed audio

SQLite

#18
0WIN / 0 LOSS
never worse
Up to 23% smaller — page-aware compression beats LZMA on database files

ELF / Binary

#19
0WIN / 0 LOSS
never worse
Up to 17% smaller — section-aware beats flat LZMA

ELF / Binary

#19
153WIN / 0 LOSS
up to 88.9% smaller
Up to 17% smaller — section-aware beats flat LZMA

BMP

#20
0WIN / 0 LOSS
never worse
Uncompressed bitmap — excellent compression potential

TIFF

#21
0WIN / 0 LOSS
never worse
Multi-layer TIFF compression with metadata separation

ICO

#22
0WIN / 0 LOSS
never worse
Multi-resolution icon compression

Fonts

#23
0WIN / 0 LOSS
never worse
Font table separation + glyph compression

AVIF

#99
0WIN / 0 LOSS
never worse

HEIC

#99
0WIN / 0 LOSS
never worse

All 3557 downloadable test files

Every filename is a link to the original source. Download any file, run xz -9 or zstd -19 yourself, and compare. We publish everything because transparency isn't a marketing term — it's how science works.

Full transparency — every file is downloadable

Click any filename to download from the original public source. Run LZMA, gzip, brotli, zstd yourself. We publish our exact sources, methodology, and results because we have nothing to hide. If you find a file where PZIP loses, we want to know.

File
Type
Size
Saving vs LZMA
Result
Source
Classifications.csvCSV427.7 KB99.0%WIN
csv2jsonl_Classifications.jsonlJSONL / NDJSON1.5 MB99.0%WIN
fivethirtyeight_bob_ross.jsonlJSONL / NDJSON466.0 KB98.8%WIN
r_count_affairs.jsonlJSONL / NDJSON180.1 KB98.8%WIN
tidytuesday_freedom.jsonlJSONL / NDJSON683.8 KB98.8%WIN
vega_birdstrikes.jsonlJSONL / NDJSON4.1 MB98.5%WIN
csv2jsonl_Geography.jsonlJSONL / NDJSON2.2 MB98.3%WIN
seaborn_titanic.jsonlJSONL / NDJSON221.8 KB98.2%WIN
worldbank_life_expectancy.jsonlJSONL / NDJSON1.5 MB97.9%WIN
tidytuesday_womens_soccer.jsonlJSONL / NDJSON71.1 KB97.7%WIN
worldbank_population.jsonlJSONL / NDJSON1.4 MB97.7%WIN
r_aer_affairs.jsonlJSONL / NDJSON108.2 KB97.6%WIN
npm_mocha.jsonJSON3.8 MB97.3%WIN
tidytuesday_frogs.jsonlJSONL / NDJSON111.3 KB97.3%WIN
elastic_nginx_sample.logLog Files6.7 MB97.3%WIN
ecb_exchange_rates.jsonlJSONL / NDJSON3.3 MB97.1%WIN
fivethirtyeight_congress_age.jsonlJSONL / NDJSON4.6 MB97.1%WIN
tidytuesday_ninja_warrior.jsonlJSONL / NDJSON122.0 KB97.1%WIN
tidytuesday_plastics.jsonlJSONL / NDJSON3.0 MB97.1%WIN
nytimes_covid_states.jsonlJSONL / NDJSON4.1 MB97.0%WIN
r_survival_colon.jsonlJSONL / NDJSON452.2 KB97.0%WIN
tidytuesday_dog_breeds.jsonlJSONL / NDJSON89.9 KB96.9%WIN
vega_weather.jsonlJSONL / NDJSON409.6 KB96.9%WIN
tidytuesday_palmer_penguins.jsonlJSONL / NDJSON59.1 KB96.8%WIN
Constituents.csvCSV1.9 MB96.7%WIN
seaborn_penguins.jsonlJSONL / NDJSON53.7 KB96.7%WIN
tidytuesday_scooby.jsonlJSONL / NDJSON1.1 MB96.7%WIN
tidytuesday_eruptions.jsonlJSONL / NDJSON4.1 MB96.6%WIN
tidytuesday_squirrels.jsonlJSONL / NDJSON2.8 MB96.5%WIN
r_wooldridge_wage1.jsonlJSONL / NDJSON214.3 KB96.4%WIN
Media_primary.csvCSV1.8 MB96.3%WIN
r_datasets_infert.jsonlJSONL / NDJSON39.1 KB96.3%WIN
tidytuesday_cocktails.jsonlJSONL / NDJSON780.5 KB96.3%WIN
tidytuesday_penguins_raw.jsonlJSONL / NDJSON157.1 KB96.3%WIN
vega_seattle_weather.jsonlJSONL / NDJSON171.2 KB96.3%WIN
gold_prices.jsonlJSONL / NDJSON88.8 KB96.2%WIN
ml_wine_white.jsonlJSONL / NDJSON1.3 MB96.2%WIN
vega_unemployment_industries.jsonlJSONL / NDJSON199.6 KB96.2%WIN
mushrooms.csvCSV365.2 KB96.1%WIN
ml_wine_red.jsonlJSONL / NDJSON424.0 KB96.1%WIN
worldbank_gdp_percapita.jsonlJSONL / NDJSON1.5 MB96.1%WIN
seaborn_taxis.jsonlJSONL / NDJSON2.1 MB96.0%WIN
tidytuesday_beyonce_taylor.jsonlJSONL / NDJSON3.9 MB96.0%WIN
plotly_tips.jsonlJSONL / NDJSON27.5 KB95.9%WIN
r_ggplot_mpg.jsonlJSONL / NDJSON46.3 KB95.9%WIN
tidytuesday_star_trek.jsonlJSONL / NDJSON649.4 KB95.9%WIN
tidytuesday_coffee.jsonlJSONL / NDJSON1.6 MB95.8%WIN
tidytuesday_marble_races.jsonlJSONL / NDJSON78.3 KB95.8%WIN
tidytuesday_tbi.jsonlJSONL / NDJSON32.3 KB95.6%WIN
calgary_access_50k.logLog Files3.4 MB95.6%WIN
fivethirtyeight_bad_drivers.jsonlJSONL / NDJSON29.3 KB95.5%WIN
tidytuesday_astronaut.jsonlJSONL / NDJSON776.8 KB95.5%WIN
tidytuesday_volcano.jsonlJSONL / NDJSON754.1 KB95.5%WIN
covid_us_counties.csvCSV1.9 MB95.4%WIN
ghcontrib_expressjs_express.jsonJSON94.2 KB95.3%WIN
ghcontrib_pallets_flask.jsonJSON93.7 KB95.3%WIN
ghcontrib_facebook_react.jsonJSON93.3 KB95.2%WIN
ghcontrib_golang_go.jsonJSON93.0 KB95.2%WIN
ghcontrib_vuejs_vue.jsonJSON93.2 KB95.2%WIN
global_temp.jsonlJSONL / NDJSON212.6 KB95.2%WIN
r_reshape_tips.jsonlJSONL / NDJSON31.7 KB95.2%WIN
tidytuesday_christmas_songs.jsonlJSONL / NDJSON137.4 KB95.2%WIN
quant.csvCSV729.8 KB95.1%WIN
ghcontrib_apache_kafka.jsonJSON94.1 KB95.1%WIN
ghcontrib_denoland_deno.jsonJSON93.4 KB95.1%WIN
ghcontrib_docker_compose.jsonJSON94.2 KB95.1%WIN
ghcontrib_elastic_elasticsearch.jsonJSON94.2 KB95.1%WIN
ghcontrib_grafana_grafana.jsonJSON95.4 KB95.1%WIN
ghcontrib_kubernetes_kubernetes.jsonJSON93.5 KB95.1%WIN
ghcontrib_microsoft_TypeScript.jsonJSON94.4 KB95.1%WIN
ghcontrib_nodejs_node.jsonJSON93.9 KB95.1%WIN
ghcontrib_prometheus_prometheus.jsonJSON94.6 KB95.1%WIN
ghcontrib_rust-lang_rust.jsonJSON93.5 KB95.1%WIN
ghcontrib_vercel_next.js.jsonJSON94.1 KB95.1%WIN
fivethirtyeight_comic_marvel.jsonlJSONL / NDJSON4.9 MB95.1%WIN
population.jsonlJSONL / NDJSON1.5 MB95.1%WIN
r_mass_birthwt.jsonlJSONL / NDJSON27.0 KB95.1%WIN
tidytuesday_passwords.jsonlJSONL / NDJSON92.9 KB95.1%WIN
ghcontrib_microsoft_vscode.jsonJSON94.2 KB95.0%WIN
r_datasets_chickweight.jsonlJSONL / NDJSON43.9 KB95.0%WIN
vega_flights_20k.jsonlJSONL / NDJSON1.9 MB94.8%WIN
bob_ross.csvCSV207.4 KB94.7%WIN
families.csvCSV671.8 KB94.7%WIN
consumer_complaints_26k.csvCSV4.5 MB94.6%WIN
nyc_baby_names.csvCSV1.9 MB94.6%WIN
ghissues_elastic_elasticsearch.jsonJSON505.9 KB94.6%WIN
ghissues_vuejs_vue.jsonJSON406.9 KB94.6%WIN
natural_gas.jsonlJSONL / NDJSON284.5 KB94.6%WIN
tidytuesday_ikea.jsonlJSONL / NDJSON1.5 MB94.6%WIN
vega_flights_10k.jsonlJSONL / NDJSON959.4 KB94.6%WIN
vega_flights_airport.jsonlJSONL / NDJSON294.6 KB94.5%WIN
Medium.csvCSV451.2 KB94.4%WIN
ghissues_kubernetes_kubernetes.jsonJSON853.8 KB94.4%WIN
Geography.csvCSV545.8 KB94.3%WIN
vega_disasters.jsonlJSONL / NDJSON48.2 KB94.3%WIN
vega_flights_5k.jsonlJSONL / NDJSON479.7 KB94.3%WIN
tidytuesday_food_consumption.jsonlJSONL / NDJSON152.9 KB94.2%WIN
ghissues_django_django.jsonJSON455.9 KB94.1%WIN
seaborn_planets.jsonlJSONL / NDJSON121.3 KB94.1%WIN
vega_movies.jsonlJSONL / NDJSON1.3 MB94.0%WIN
census_income.csvCSV3.7 MB93.9%WIN
fivethirtyeight_steak_survey.csvCSV61.2 KB93.9%WIN
ghissues_torvalds_linux.jsonJSON349.5 KB93.9%WIN
fivethirtyeight_daily_show.jsonlJSONL / NDJSON355.0 KB93.9%WIN
vega_cars.jsonlJSONL / NDJSON76.7 KB93.9%WIN
npm_next.jsonJSON27.6 MB93.8%WIN
usgs_earthquakes_week.jsonlJSONL / NDJSON1.4 MB93.8%WIN
ghissues_grafana_grafana.jsonJSON569.1 KB93.7%WIN
ghissues_python_cpython.jsonJSON454.6 KB93.7%WIN
r_ggplot_txhousing.jsonlJSONL / NDJSON1.6 MB93.7%WIN
tidytuesday_cran_packages.jsonlJSONL / NDJSON4.1 MB93.7%WIN
tidytuesday_tuition.jsonlJSONL / NDJSON786.5 KB93.7%WIN
tidytuesday_media_franchises.jsonlJSONL / NDJSON73.4 KB93.6%WIN
vega_flights_2k.jsonlJSONL / NDJSON191.9 KB93.6%WIN
jsonl2csv_ecb_exchange_rates.csvCSV1.4 MB93.5%WIN
tidytuesday_beer_awards.jsonlJSONL / NDJSON873.1 KB93.5%WIN
clarknet_access_aug28_50k.logLog Files4.9 MB93.5%WIN
exchange_rates.csvCSV1.9 MB93.4%WIN
titanic_seaborn.csvCSV55.7 KB93.4%WIN
tidytuesday_ramen.jsonlJSONL / NDJSON455.2 KB93.4%WIN
access.logLog Files5.2 MB93.4%WIN
adult_income.csvCSV1.9 MB93.3%WIN
countries-data.jsonJSON1.3 MB93.3%WIN
npm_sequelize.jsonJSON4.1 MB93.3%WIN
r_datasets_treering.jsonlJSONL / NDJSON430.3 KB93.3%WIN
vega_co2_concentration.jsonlJSONL / NDJSON47.8 KB93.3%WIN
ssa_names_national.csvCSV1.9 MB93.1%WIN
seaborn_dots.jsonlJSONL / NDJSON83.9 KB93.1%WIN
vega_countries.jsonlJSONL / NDJSON96.5 KB93.1%WIN
ghissues_prometheus_prometheus.jsonJSON524.5 KB92.7%WIN
lemurs.csvCSV1.9 MB92.6%WIN
ghissues_apache_kafka.jsonJSON399.6 KB92.6%WIN
r_survival_pbc.jsonlJSONL / NDJSON137.3 KB92.6%WIN
jsonl2csv_fivethirtyeight_bob_ross.csvCSV65.2 KB92.5%WIN
npm_webpack.jsonJSON4.7 MB92.5%WIN
bob_ross_538.csvCSV64.8 KB92.4%WIN
CMakeOutput.logLog Files43.7 KB92.4%WIN
olympics.csvCSV1.9 MB92.3%WIN
ghissues_microsoft_vscode.jsonJSON704.8 KB92.3%WIN
s_and_p_500.jsonlJSONL / NDJSON426.6 KB92.3%WIN
jsonl2csv_hadley_baby_names.csvCSV1.2 MB92.2%WIN
natural_gas_prices.csvCSV120.5 KB92.2%WIN
ghissues_pallets_flask.jsonJSON458.1 KB92.2%WIN
jsonl2csv_fivethirtyeight_congress_age.csvCSV1.3 MB92.1%WIN
npm_gatsby.jsonJSON22.5 MB92.1%WIN
fivethirtyeight_congress.csvCSV1.3 MB92.0%WIN
jhu_covid_deaths_global.csvCSV1.2 MB92.0%WIN
nytimes_covid_states.csvCSV2.1 MB92.0%WIN
npm_rollup.jsonJSON5.8 MB92.0%WIN
fivethirtyeight_avengers.jsonlJSONL / NDJSON83.0 KB91.9%WIN
world_cities.jsonlJSONL / NDJSON3.1 MB91.9%WIN
r_mass_crabs.jsonlJSONL / NDJSON24.6 KB91.8%WIN
covid_us_states.csvCSV1.9 MB91.7%WIN
ghissues_vercel_next.js.jsonJSON422.4 KB91.7%WIN
npm_redux.jsonJSON540.9 KB91.7%WIN
country_codes.jsonlJSONL / NDJSON438.1 KB91.7%WIN
csv2jsonl_Cxx17Issues.jsonlJSONL / NDJSON95.2 KB91.6%WIN
ghissues_denoland_deno.jsonJSON387.3 KB91.5%WIN
seaborn_dowjones.jsonlJSONL / NDJSON26.3 KB91.5%WIN
vega_population.jsonlJSONL / NDJSON30.4 KB91.4%WIN
git_paudio_stat.logLog Files849.9 KB91.4%WIN
fivethirtyeight_hate_crimes.jsonlJSONL / NDJSON21.2 KB91.3%WIN
vega_gapminder.jsonlJSONL / NDJSON72.8 KB91.3%WIN
stress_10k.csvCSV1.3 MB91.2%WIN
csv2jsonl_Cxx23Issues.jsonlJSONL / NDJSON94.4 KB91.2%WIN
ghissues_docker_compose.jsonJSON544.6 KB91.1%WIN
npm_rxjs.jsonJSON957.1 KB91.0%WIN
r_mass_boston.jsonlJSONL / NDJSON118.2 KB91.0%WIN
csv2jsonl_Cxx20Issues.jsonlJSONL / NDJSON93.4 KB90.9%WIN
plotly_gapminder.jsonlJSONL / NDJSON279.5 KB90.9%WIN
jsonl2csv_gold_prices.csvCSV36.9 KB90.6%WIN
taxis.csvCSV849.0 KB90.6%WIN
npm_eslint.jsonJSON1.8 MB90.6%WIN
currency_codes.jsonlJSONL / NDJSON63.4 KB90.6%WIN
r_ggplot_economics.jsonlJSONL / NDJSON74.1 KB90.6%WIN
global_temp_monthly.csvCSV82.0 KB90.5%WIN
gold_prices_monthly.csvCSV34.7 KB90.5%WIN
npm_knex.jsonJSON1.3 MB90.5%WIN
csv2jsonl_Cxx2cIssues.jsonlJSONL / NDJSON78.9 KB90.5%WIN
npm_nx.jsonJSON10.0 MB90.4%WIN
csv2jsonl_Cxx2cPapers.jsonlJSONL / NDJSON50.3 KB90.4%WIN
fivethirtyeight_bechdel.jsonlJSONL / NDJSON600.3 KB90.4%WIN
coffee_ratings.csvCSV589.5 KB90.3%WIN
ghissues_facebook_react.jsonJSON509.1 KB90.3%WIN
ghissues_golang_go.jsonJSON592.7 KB90.2%WIN
ghissues_nodejs_node.jsonJSON659.3 KB90.1%WIN
schemaorg-current.jsonJSON1.5 MB90.1%WIN
ghissues_microsoft_TypeScript.jsonJSON616.0 KB90.0%WIN
r_dplyr_starwars.jsonlJSONL / NDJSON20.5 KB90.0%WIN
tidytuesday_moore_law.jsonlJSONL / NDJSON27.5 KB90.0%WIN
penguins_raw.csvCSV51.9 KB89.8%WIN
npm_axios.jsonJSON733.9 KB89.8%WIN
npm_nuxt.jsonJSON1.5 MB89.8%WIN
tidytuesday_simpsons.jsonlJSONL / NDJSON186.4 KB89.8%WIN
npm_express.jsonJSON782.4 KB89.7%WIN
npm_pm2.jsonJSON1.0 MB89.7%WIN
population.csvCSV526.0 KB89.6%WIN
tidytuesday_wheels.jsonlJSONL / NDJSON41.0 KB89.6%WIN
nba_players.csvCSV162.5 KB89.5%WIN
tidytuesday_transit_cost.jsonlJSONL / NDJSON250.3 KB89.5%WIN
inflation_us.csvCSV28.0 KB89.4%WIN
plotly_gapminderdata.jsonlJSONL / NDJSON216.4 KB89.4%WIN
tidytuesday_the_office.jsonlJSONL / NDJSON23.7 KB89.4%WIN
daily-temperatures.csvCSV66.3 KB89.3%WIN
Titles.csvCSV1.2 MB89.2%WIN
comic_characters.csvCSV1.1 MB89.2%WIN
lockfile_microsoft_TypeScript.jsonJSON376.9 KB89.2%WIN
sys_ld.elfELF / Binary1.7 MB88.9%WIN
tornados.csvCSV1.9 MB88.8%WIN
npm_typescript.jsonJSON14.5 MB88.8%WIN
git_pvideo_stat.logLog Files776.9 KB88.7%WIN
uber_rides_raw.csvCSV1.9 MB88.6%WIN
npm_mysql2.jsonJSON627.4 KB88.5%WIN
plotly_apple_finance.jsonlJSONL / NDJSON136.8 KB88.5%WIN
jsonl2csv_fivethirtyeight_comic_marvel.csvCSV2.3 MB88.4%WIN
slaughter_house.csvCSV1.9 MB88.4%WIN
diamonds.csvCSV1.9 MB88.3%WIN
fivethirtyeight_marvel_wikia.csvCSV2.3 MB88.3%WIN
tidytuesday_bechdel.jsonlJSONL / NDJSON1.8 MB88.3%WIN
npm_webpack-dev-server.jsonJSON826.7 KB88.1%WIN
volcano_eruptions.csvCSV1.2 MB87.9%WIN
npm_vue.jsonJSON1.9 MB87.9%WIN
vega_airports.jsonlJSONL / NDJSON505.4 KB87.8%WIN
us_zip_codes.csvCSV1.2 MB87.5%WIN
drugs_fda.csvCSV1.7 MB87.4%WIN
Cxx17Issues.csvCSV63.0 KB87.3%WIN
pset_definitions.csvCSV117.6 KB87.3%WIN
numbats.csvCSV182.7 KB87.1%WIN
npm_mobx.jsonJSON1007.1 KB87.1%WIN
language_codes.jsonlJSONL / NDJSON50.0 KB87.1%WIN
Cxx23Issues.csvCSV63.0 KB87.0%WIN
npm_prettier.jsonJSON638.4 KB86.9%WIN
plastic_pollution.csvCSV724.6 KB86.8%WIN
water_access.csvCSV1.9 MB86.8%WIN
npm_cypress.jsonJSON1.0 MB86.7%WIN
astronauts.csvCSV213.9 KB86.5%WIN
Cxx20Issues.csvCSV62.3 KB86.4%WIN
npm_moment.jsonJSON235.9 KB86.4%WIN
npm_ts-node.jsonJSON561.1 KB86.3%WIN
fivethirtyeight_riddler.jsonlJSONL / NDJSON646.0 KB86.3%WIN
r_ggplot_midwest.jsonlJSONL / NDJSON304.6 KB86.1%WIN
npm_d3.jsonJSON715.7 KB86.0%WIN
npm_three.jsonJSON888.7 KB86.0%WIN
pypi_sqlalchemy.jsonJSON3.9 MB85.9%WIN
jsonl2csv_jhu_covid_confirmed_global.csvCSV1.7 MB85.8%WIN
npm_puppeteer.jsonJSON3.8 MB85.8%WIN
jhu_covid_confirmed.csvCSV1.7 MB85.7%WIN
pypi_aiohttp.jsonJSON7.3 MB85.7%WIN
Cxx2cIssues.csvCSV51.8 KB85.6%WIN
npm_sass-loader.jsonJSON413.5 KB85.5%WIN
tidytuesday_solar.jsonlJSONL / NDJSON26.3 KB85.5%WIN
pypi_matplotlib.jsonJSON1.9 MB85.4%WIN
pypi_numpy.jsonJSON2.7 MB85.4%WIN
pypi_pillow.jsonJSON2.6 MB85.4%WIN
npm_babel-loader.jsonJSON245.3 KB85.3%WIN
pypi_mypy.jsonJSON1.3 MB85.2%WIN
pypi_scikit_learn.jsonJSON1.1 MB85.2%WIN
npm_bcrypt.jsonJSON200.6 KB85.1%WIN
npm_webpack-cli.jsonJSON521.9 KB85.1%WIN
pypi_tensorflow.jsonJSON1.3 MB85.1%WIN
tidytuesday_product_hunt.jsonlJSONL / NDJSON2.6 MB85.1%WIN
pypi_scipy.jsonJSON1.8 MB85.0%WIN
Cxx2cPapers.csvCSV32.0 KB84.9%WIN
npm_body-parser.jsonJSON211.7 KB84.9%WIN
npm_inquirer.jsonJSON576.6 KB84.9%WIN
pypi_pandas.jsonJSON1.7 MB84.9%WIN
npm_serve-static.jsonJSON146.9 KB84.8%WIN
pypi_tornado.jsonJSON371.3 KB84.8%WIN
npm_ts-loader.jsonJSON600.5 KB84.7%WIN
plotly_us_cities.jsonlJSONL / NDJSON112.2 KB84.7%WIN
tidytuesday_solar_wind.jsonlJSONL / NDJSON26.0 KB84.7%WIN
npm_css-loader.jsonJSON504.2 KB84.5%WIN
npm_uuid.jsonJSON219.3 KB84.4%WIN
pypi_boto3.jsonJSON2.7 MB84.4%WIN
fivethirtyeight_nba_raptor.jsonlJSONL / NDJSON3.1 MB84.4%WIN
npm_nodemon.jsonJSON636.9 KB84.3%WIN
npm_html-webpack-plugin.jsonJSON481.9 KB84.2%WIN
npm_yargs.jsonJSON725.6 KB84.2%WIN
fips_codes.csvCSV77.5 KB83.9%WIN
gps_track_10k.csvCSV546.2 KB83.9%WIN
ufo_sightings.csvCSV1.9 MB83.8%WIN
npm_compression.jsonJSON96.2 KB83.8%WIN
npm_express-validator.jsonJSON302.4 KB83.8%WIN
npm_jsonwebtoken.jsonJSON179.4 KB83.7%WIN
wapo_police_shootings.csvCSV1.8 MB83.6%WIN
npm_jimp.jsonJSON1.1 MB83.6%WIN
npm_mini-css-extract-plugin.jsonJSON387.3 KB83.6%WIN
plotly_flights_2k.jsonlJSONL / NDJSON35.7 KB83.6%WIN
npm_lodash.jsonJSON238.5 KB83.5%WIN
shlib_libxcb-dri3_so_0_0_0.elfELF / Binary22.2 KB83.4%WIN
npm_listr2.jsonJSON1020.9 KB83.4%WIN
npm_postcss-loader.jsonJSON271.8 KB83.4%WIN
pypi_django.jsonJSON536.0 KB83.4%WIN
pypi_black.jsonJSON676.7 KB83.3%WIN
pypi_pytest.jsonJSON252.6 KB83.3%WIN
pypi_sphinx.jsonJSON390.8 KB83.3%WIN
fail2ban_sshd.logLog Files35.5 KB83.2%WIN
co2_emissions.csvCSV1.9 MB83.1%WIN
us_broadband.csvCSV104.1 KB83.1%WIN
npm_lerna.jsonJSON862.8 KB83.1%WIN
npm_nodemailer.jsonJSON549.9 KB83.1%WIN
pypi_fastapi.jsonJSON408.3 KB83.1%WIN
pypi_requests.jsonJSON168.8 KB83.0%WIN
wine_quality_red.csvCSV98.6 KB82.9%WIN
npm_bull.jsonJSON602.0 KB82.9%WIN
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restav@2x.webpWebP129.0 KB0%ERROR
sqlite-nvim-ok.webpWebP63.4 KB0%ERROR
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wezterm-font.webpWebP66.6 KB0%ERROR
yomiuri-land-thunbmail.webpWebP87.8 KB0%ERROR
boe_yield_curve.xlsxExcel (XLSX)232.2 KB0%LOSS
csv2xlsx_imdb_top250.xlsxExcel (XLSX)857.5 KB0%WIN
csv2xlsx_netflix_titles.xlsxExcel (XLSX)1.1 MB0%WIN
csv2xlsx_noaa_hourly_weather.xlsxExcel (XLSX)3.3 MB0%ERROR
csv2xlsx_numbats.xlsxExcel (XLSX)79.8 KB0%ERROR
csv2xlsx_nyc_311.xlsxExcel (XLSX)2.9 MB0%ERROR
csv2xlsx_nyc_baby_names.xlsxExcel (XLSX)1.3 MB0%ERROR
csv2xlsx_nyc_taxi_2014.xlsxExcel (XLSX)3.6 MB0%ERROR
csv2xlsx_nytimes_covid_counties.xlsxExcel (XLSX)1.4 MB0%ERROR
csv2xlsx_nytimes_covid_counties_2020.xlsxExcel (XLSX)1.4 MB0%ERROR
csv2xlsx_nytimes_covid_states.xlsxExcel (XLSX)1.3 MB0%ERROR
csv2xlsx_olympics.xlsxExcel (XLSX)1020.9 KB0%ERROR
csv2xlsx_owid_co2.xlsxExcel (XLSX)15.8 MB0%ERROR
csv2xlsx_owid_co2_data.xlsxExcel (XLSX)15.8 MB0%ERROR
csv2xlsx_owid_covid_data.xlsxExcel (XLSX)11.2 MB0%ERROR
csv2xlsx_owid_covid_world.xlsxExcel (XLSX)5.2 MB0%ERROR
csv2xlsx_owid_energy.xlsxExcel (XLSX)11.1 MB0%ERROR
csv2xlsx_penguins_raw.xlsxExcel (XLSX)32.4 KB0%ERROR
csv2xlsx_plastic_pollution.xlsxExcel (XLSX)703.9 KB0%ERROR
csv2xlsx_plotly_precipitation.xlsxExcel (XLSX)332.8 KB0%ERROR
csv2xlsx_pokemon.xlsxExcel (XLSX)78.3 KB0%ERROR
csv2xlsx_police_killings.xlsxExcel (XLSX)113.0 KB0%ERROR
csv2xlsx_population.xlsxExcel (XLSX)375.9 KB0%ERROR
csv2xlsx_pset_definitions.xlsxExcel (XLSX)31.3 KB0%ERROR
csv2xlsx_quant.xlsxExcel (XLSX)297.0 KB0%ERROR
csv2xlsx_runways.xlsxExcel (XLSX)4.2 MB0%ERROR
csv2xlsx_scientific_measurements.xlsxExcel (XLSX)264.7 KB0%ERROR
csv2xlsx_senators_twitter.xlsxExcel (XLSX)1.0 MB0%ERROR
csv2xlsx_slaughter_house.xlsxExcel (XLSX)986.0 KB0%ERROR
csv2xlsx_snakes_count_10000.xlsxExcel (XLSX)126.0 KB0%ERROR
csv2xlsx_sp500.xlsxExcel (XLSX)37.6 KB0%ERROR
csv2xlsx_sp500_constituents.xlsxExcel (XLSX)37.6 KB0%ERROR
csv2xlsx_sp500_data.xlsxExcel (XLSX)107.4 KB0%ERROR
csv2xlsx_spam.xlsxExcel (XLSX)280.9 KB0%ERROR
csv2xlsx_spotify_songs.xlsxExcel (XLSX)1.5 MB0%ERROR
csv2xlsx_ssa_names_national.xlsxExcel (XLSX)971.6 KB0%ERROR
eia_energy_data.xlsxExcel (XLSX)74.5 KB0%ERROR
employee_database_10k.xlsxExcel (XLSX)804.0 KB0%ERROR
financial_ledger_50k.xlsxExcel (XLSX)3.0 MB0%ERROR
financial_sample.xlsxExcel (XLSX)81.5 KB0%ERROR
github_pokemon.xlsxExcel (XLSX)78.7 KB0%ERROR
indian_states_cities.xlsxExcel (XLSX)67.7 KB0%ERROR
inventory_sparse_20k.xlsxExcel (XLSX)1010.2 KB0%ERROR
large_5000x10_seq.xlsxExcel (XLSX)158.6 KB0%ERROR
margin_percentage.xlsxExcel (XLSX)27.5 KB0%ERROR
medium_1000x10_mult.xlsxExcel (XLSX)49.5 KB0%ERROR
multi_sheet_sales.xlsxExcel (XLSX)1.0 MB0%ERROR
ons_gdp_uk.xlsxExcel (XLSX)1.8 MB0%ERROR
price.xlsxExcel (XLSX)32.2 KB0%ERROR
quantity.xlsxExcel (XLSX)51.2 KB0%ERROR
repeat2.xlsxExcel (XLSX)39.2 KB0%ERROR
sequential_ids_100k.xlsxExcel (XLSX)2.0 MB0%ERROR
supermarket_sales.xlsxExcel (XLSX)120.9 KB0%ERROR
supermarket_sales_data.xlsxExcel (XLSX)124.8 KB0%ERROR
timeseries_patterns.xlsxExcel (XLSX)2.1 MB0%ERROR
wide_matrix_500col.xlsxExcel (XLSX)2.9 MB0%ERROR
worldbank_country_data.xlsxExcel (XLSX)66.6 KB0%ERROR
atom_github_angular.xmlXML59.8 KB0%ERROR
atom_github_deno.xmlXML145.8 KB0%ERROR
atom_github_docker.xmlXML26.8 KB0%ERROR
atom_github_node.xmlXML349.1 KB0%ERROR
atom_github_rails.xmlXML411.8 KB0%ERROR
atom_github_terraform.xmlXML58.7 KB0%ERROR
cnErrors.XMLXML21.7 KB0%ERROR
csv2xml_Attributes.xmlXML1.0 MB0%ERROR
csv2xml_Classifications.xmlXML666.1 KB0%ERROR
csv2xml_Constituents.xmlXML3.8 MB0%ERROR
csv2xml_Cxx17Issues.xmlXML130.7 KB0%ERROR
csv2xml_Cxx20Issues.xmlXML127.2 KB0%ERROR
dotnet-directory-build.propsXML30.6 KB0%ERROR
gh_ant_build_main.xmlXML85.4 KB0%ERROR
gh_camel_parent.xmlXML197.7 KB0%ERROR
gh_camel_pom.xmlXML48.2 KB0%ERROR
gh_checkstyle_checks.xmlXML23.5 KB0%ERROR
gh_checkstyle_pom.xmlXML247.2 KB0%ERROR
gh_cldr_af.xmlXML388.1 KB0%ERROR
gh_cldr_am.xmlXML471.8 KB0%ERROR
gh_cldr_arabic.xmlXML704.4 KB0%ERROR
gh_cldr_az.xmlXML428.7 KB0%ERROR
gh_cldr_bengali.xmlXML541.3 KB0%ERROR
gh_cldr_bs.xmlXML822.8 KB0%ERROR
gh_cldr_catalan.xmlXML481.6 KB0%ERROR
gh_cldr_croatian.xmlXML622.3 KB0%ERROR
gh_cldr_cy.xmlXML640.7 KB0%ERROR
gh_cldr_danish.xmlXML469.5 KB0%ERROR
gh_cldr_dutch.xmlXML860.8 KB0%ERROR
gh_cldr_en_gb.xmlXML474.2 KB0%ERROR
gh_cldr_english.xmlXML467.8 KB0%ERROR
gh_cldr_es_ar.xmlXML21.5 KB0%ERROR
gh_cldr_es_mx.xmlXML387.7 KB0%ERROR
gh_cldr_et.xmlXML437.6 KB0%ERROR
gh_cldr_eu.xmlXML764.0 KB0%ERROR
gh_cldr_filipino.xmlXML505.6 KB0%ERROR
gh_cldr_finnish.xmlXML559.1 KB0%ERROR
gh_cldr_fr_ca.xmlXML428.8 KB0%ERROR
gh_cldr_french.xmlXML561.6 KB0%ERROR
gh_cldr_ga.xmlXML626.1 KB0%ERROR
gh_cldr_german.xmlXML597.7 KB0%ERROR
gh_cldr_gl.xmlXML404.8 KB0%ERROR
gh_cldr_greek.xmlXML556.5 KB0%ERROR
gh_cldr_gu.xmlXML495.7 KB0%ERROR
gh_cldr_hindi.xmlXML551.8 KB0%ERROR
gh_cldr_hy.xmlXML499.7 KB0%ERROR
gh_cldr_ig.xmlXML344.8 KB0%ERROR
gh_cldr_indonesian.xmlXML465.4 KB0%ERROR
gh_cldr_italian.xmlXML433.8 KB0%ERROR
gh_cldr_japanese.xmlXML498.8 KB0%ERROR
gh_cldr_ka.xmlXML491.7 KB0%ERROR
gh_cldr_kk.xmlXML486.9 KB0%ERROR
gh_cldr_km.xmlXML398.7 KB0%ERROR
gh_cldr_kn.xmlXML667.8 KB0%ERROR
gh_cldr_korean.xmlXML492.5 KB0%ERROR
gh_cldr_likely_subtags.xmlXML898.0 KB0%ERROR
gh_cldr_lo.xmlXML458.4 KB0%ERROR
gh_cldr_lv.xmlXML589.3 KB0%ERROR
gh_cldr_malay.xmlXML449.4 KB0%ERROR
gh_cldr_metazones.xmlXML92.8 KB0%ERROR
gh_cldr_mk.xmlXML514.7 KB0%ERROR
gh_cldr_ml.xmlXML639.5 KB0%ERROR
gh_cldr_mr.xmlXML657.9 KB0%ERROR
gh_cldr_my.xmlXML403.9 KB0%ERROR
gh_cldr_ne.xmlXML464.7 KB0%ERROR
gh_cldr_pa.xmlXML510.9 KB0%ERROR
gh_cldr_polish.xmlXML760.7 KB0%ERROR
gh_cldr_portuguese.xmlXML489.2 KB0%ERROR
gh_cldr_pt_br.xmlXML422.6 KB0%ERROR
gh_cldr_romanian.xmlXML580.7 KB0%ERROR
gh_cldr_root.xmlXML262.1 KB0%ERROR
gh_cldr_russian.xmlXML946.7 KB0%ERROR
gh_cldr_serbian.xmlXML647.2 KB0%ERROR
gh_cldr_si.xmlXML455.5 KB0%ERROR
gh_cldr_sk.xmlXML795.9 KB0%ERROR
gh_cldr_sl.xmlXML745.7 KB0%ERROR
gh_cldr_spanish.xmlXML474.6 KB0%ERROR
gh_cldr_sr_latn.xmlXML591.6 KB0%ERROR
gh_cldr_supplemental_meta.xmlXML176.0 KB0%ERROR
gh_cldr_sw.xmlXML397.3 KB0%ERROR
gh_cldr_swedish.xmlXML537.4 KB0%ERROR
gh_cldr_tamil.xmlXML603.5 KB0%ERROR
gh_cldr_te.xmlXML527.1 KB0%ERROR
gh_cldr_thai.xmlXML528.5 KB0%ERROR
gh_cldr_transforms.xmlXML25.4 KB0%ERROR
gh_cldr_turkish.xmlXML469.0 KB0%ERROR
gh_cldr_ukrainian.xmlXML827.1 KB0%ERROR
gh_cldr_ur.xmlXML444.0 KB0%ERROR
gh_cldr_vietnamese.xmlXML491.6 KB0%ERROR
gh_cldr_windowszones.xmlXML48.2 KB0%ERROR
gh_cldr_yo.xmlXML337.4 KB0%ERROR
gh_cldr_zh_hant.xmlXML727.7 KB0%ERROR
gh_cldr_zu.xmlXML421.0 KB0%ERROR
gh_eclipse_collections_pom.xmlXML56.4 KB0%ERROR
gh_geonames_readme.xmlXML381.3 KB0%ERROR
gh_gh_keycloak_pom.xmlXML77.2 KB0%ERROR
gh_gh_pmd_pom2.xmlXML64.9 KB0%ERROR
gh_pmd_java_codestyle.xmlXML84.4 KB0%ERROR
gh_pmd_java_design.xmlXML54.3 KB0%ERROR
gh_pmd_java_errorprone.xmlXML134.1 KB0%ERROR
gh_pmd_java_performance.xmlXML36.9 KB0%ERROR
gh_pmd_java_rules.xmlXML88.6 KB0%ERROR
gh_spotbugs_findbugs.xmlXML110.9 KB0%ERROR
gh_testng_suite.xmlXML32.3 KB0%ERROR
gh_tomcat_web_xml.xmlXML168.7 KB0%ERROR
gh_wildfly_ee_pom.xmlXML137.7 KB0%ERROR
gh_wildfly_pom.xmlXML118.3 KB0%ERROR
hibernate-mapping.xsdXML144.8 KB0%ERROR
maven_activemq-parent-6.0.0.xmlXML56.1 KB0%ERROR
maven_apache-maven-3.9.6.xmlXML28.7 KB0%ERROR
maven_apache-parent-31.xmlXML23.0 KB0%ERROR
maven_avro-parent-1.11.3.xmlXML23.8 KB0%ERROR
maven_aws-bom-2.21.0.xmlXML86.4 KB0%ERROR
maven_camel-parent-4.0.0.xmlXML118.2 KB0%ERROR
maven_cassandra-parent-4.1.3.xmlXML30.0 KB0%ERROR
maven_commons-parent-65.xmlXML76.1 KB0%ERROR
maven_curator-5.6.0.xmlXML38.8 KB0%ERROR
maven_cxf-bom-4.0.3.xmlXML38.1 KB0%ERROR
maven_cxf-parent-4.0.3.xmlXML106.9 KB0%ERROR
maven_druid-28.0.0.xmlXML99.0 KB0%ERROR
maven_flink-parent-1.18.0.xmlXML73.4 KB0%ERROR
maven_hadoop-project-3.3.6.xmlXML92.0 KB0%ERROR
maven_ignite-parent-2.16.0.xmlXML46.3 KB0%ERROR
maven_jboss-parent-43.xmlXML73.7 KB0%ERROR
maven_logging-parent-10.0.0.xmlXML49.9 KB0%ERROR
maven_micronaut-bom-4.2.0.xmlXML23.3 KB0%ERROR
maven_pom.xmlXML45.1 KB0%ERROR
maven_quarkus-bom-3.6.0.xmlXML411.6 KB0%ERROR
maven_shiro-parent-1.13.0.xmlXML74.1 KB0%ERROR
maven_spring-boot-3.2.0.xmlXML108.7 KB0%ERROR
maven_struts-parent-6.3.0.xmlXML45.1 KB0%ERROR
maven_surefire-3.2.2.xmlXML20.9 KB0%ERROR
maven_tika-parent-2.9.1.xmlXML46.8 KB0%ERROR
maven_weld-parent-45.xmlXML52.4 KB0%ERROR
maven_wildfly-parent-30.0.0.xmlXML94.4 KB0%ERROR
rss_bbc_news.xmlXML27.8 KB0%ERROR
rss_bbc_tech.xmlXML44.6 KB0%ERROR
rss_bbc_world.xmlXML21.1 KB0%ERROR
rss_lwn.xmlXML21.3 KB0%ERROR
rss_phoronix.xmlXML20.3 KB0%ERROR
spring-jms.xsdXML26.1 KB0%ERROR
spring_beans_5.3.xmlXML43.4 KB0%ERROR
spring_mvc_5.3.xmlXML60.6 KB0%ERROR
synth-data-chart.svgXML96.6 KB0%ERROR
synth-drawing.svgXML66.3 KB0%ERROR
synth-enterprise-schema.xsdXML221.3 KB0%ERROR
synth-world-poi.kmlXML280.8 KB0%ERROR
synth-wsdl-services.wsdlXML187.3 KB0%ERROR
synth-xhtml-docs.xhtmlXML143.3 KB0%ERROR
w3c_xhtml1_strict.xmlXML63.9 KB0%ERROR
w3c_xhtml1_transitional.xmlXML80.8 KB0%ERROR
w3c_xmlschema.xmlXML85.6 KB0%ERROR
3557 files shown — All benchmarked against LZMA-9 (maximum compression)RTC verified: byte-exact round-trip on every file

Think you can stump us? Send your toughest file.

We're 964-1 and frankly getting a little bored. We've compressed government data, genomes, PDFs, PowerPoints, GIFs, font files, and Linux binaries. The champion belt is getting heavy. Help us find a worthy opponent.

Seriously though — if you have a file type or dataset where you think LZMA wins, we want it. We'll benchmark it and publish the results publicly, win or lose. That's the deal.

We'll benchmark your file against LZMA-9 and 5 other compressors, then publish the full results — no matter what.

Methodology

Baseline:     LZMA-9 (maximum compression level)
Also tested:  gzip-9, bz2-9, brotli-11, zstd-19, PPMd 2-24
Verification: Byte-exact round-trip on every file
Guarantee:    Never-worse (PZIP <= LZMA always)
File types:   27 (JPEG, PNG, PDF, HTML, CSS, JS, CSV, JSONL, XML, ...)
Tests:        1954 automated, zero manual overrides
Container:    .pz format (zero-overhead passthrough)
Data source:  All files from public datasets (linked in table)
API:          GET /api/benchmarks (JSON, 1-hour cache)
Date:         2026-02-15

About that Weissman Score...

Remember the Weissman Score from Silicon Valley? It measures compression ratio times the log of speed. The show made it up for TV drama.

We made it real. Except ours is verified on 3557 real-world files across 27 file types, not fictional middleware. Unlike Pied Piper, we have 46 papers and you can download every test file. They had...enthusiasm.

Also, Pied Piper had a Weissman Score of 5.2. We have a win rate of 98%. Which one would you ship to production?

"This guy compresses." — probably Russ Hanneman, if he could read benchmark tables

Download the test data

Every benchmark above uses public data. Download the same files, run any compressor you want, and verify our numbers yourself. No trust required — just math.

How we benchmark (full process)

1. We download real files from public sources

Every test file comes from a real-world public dataset — US Census, NOAA, Project Gutenberg, OWID, RFC archives. We link to the exact source so you can download the same file. No synthetic data. No cherry-picking.

2. We benchmark against 6 compressors

Every file is compressed with: PZIP, LZMA-9 (baseline), gzip-9, bz2-9, brotli-11, and zstd-19. All at maximum compression settings. We report PZIP vs the LZMA-9 baseline.

3. We verify byte-exact round-trip

Every compressed file is decompressed and compared byte-for-byte with the original.decompress(compress(X)) == Xmust pass or the result is rejected.

4. We publish everything

Source URL, file size, compression ratio, codec used, win/tie/loss status. All data is stored in Firestore and served via API. You can query/api/benchmarks yourself.

Want to test on your own data? Head to the demo page and upload any file. We'll compress it with PZIP, and you can run LZMA, gzip, bzip2, zstd, and xz right there to compare.