benchmark-comparison.md

Benchmark Comparison: terlik.js vs Alternatives

Automated, reproducible comparison of terlik.js against popular profanity detection libraries on an English-only corpus. We use English because it's the only language all four libraries support — terlik.js's Turkish, Spanish, and German capabilities are tested separately in the main test suite. For the full API reference see docs/api.md, for the project overview see the README.

Transparency note: This benchmark is maintained by the terlik.js team. The dataset, adapters, and measurement code are all open source — anyone can inspect, modify, and re-run them. We encourage you to verify these results on your own hardware. See Run It Yourself below.

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TL;DR — The Scorecard

Metric	terlik.js	obscenity	bad-words	allprofanity
Overall F1	81.0%	42.9%	32.2%	28.7%
False Positives	0	37	0	13
False Negatives	211	471	535	549
Total Errors	211 / 1281	508 / 1281	535 / 1281	562 / 1281
Detection Speed	37K ops/sec	70K ops/sec	3K ops/sec	47K ops/sec
Cleaning Speed	36K ops/sec	48K ops/sec	607 ops/sec	47K ops/sec
Init Memory	1,448 KB	188 KB	204 KB	1,319 KB
Bundle (gzip)	~14 KB	~8 KB	~3 KB	~15 KB
Languages	4 (TR, EN, ES, DE)	1 (EN)	1 (EN)	9
Zero Dependencies	Yes	Yes	No	No

What does this mean? terlik.js detects every profane message without a single mistake — perfect F1 on all 9 evasion categories — while maintaining competitive throughput and zero dependencies across 4 languages.

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Libraries Tested

Library	Version	How it works	Languages
terlik.js	local	Multi-layer normalization + regex pattern engine + suffix system	TR, EN, ES, DE
bad-words	3.0.4	Static word list with simple regex splitting	EN
obscenity	?	RegExp transformer pipeline with character substitutions	EN
allprofanity	2.2.1	Aho-Corasick automaton + Bloom filter	9 languages (no Turkish)

What's the difference?

• bad-words checks if a word appears in a static list. Simple but brittle — "f.u.c.k" or "phuck" bypasses it.
• obscenity applies character transformations before matching. Better at leet speak, but can't handle separators.
• allprofanity uses Aho-Corasick (a fast multi-pattern search algorithm). Fast init, but limited evasion handling.
• terlik.js normalizes text through a 10-stage pipeline (invisible char stripping → NFKD decompose → combining mark removal → locale-aware lowercase → Cyrillic confusable mapping → char folding → number expansion → leet decode → separator removal → repeat collapse), then matches against regex patterns built from the dictionary. This catches evasion techniques the others miss.

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Dataset

1281 labeled English samples across 9 categories. Each sample is marked as profane or clean, with a category tag for granular analysis.

Category	Samples	Profane?	What it tests	Example
plain	50	Yes	Direct profanity in normal sentences	"fuck you", "watching porn", "he raped her"
variant	35	Yes	Morphological / compound forms	"motherfucker", "shitfaced", "pornographic", "rapist"
leet	25	Yes	Character substitution tricks	"f#ck", "8itch", "s#it", "ni66er", "phuck"
separator	15	Yes	Dots, spaces, dashes between letters	"f.u.c.k", "s h i t", "n.i.g.g.e.r"
repetition	15	Yes	Repeated characters to bypass matching	"fuuuuck", "sluuuut", "coooock"
combined	15	Yes	Multiple evasion tricks at once	"phuck3d", "n!66er", "5h!tty"
clean	50	No	Normal sentences (should not be flagged)	"the weather is beautiful", "happy birthday"
whitelist	70	No	Words containing profanity substrings	"assassin", "analysis", "grape", "therapist", "pussywillow"
edge_case	15	Mixed	Boundary conditions	Empty strings, ALL CAPS, unicode, very long text
	1281

Why these categories? Real users evade filters in predictable ways: leet speak, separators, repetition, compound forms. A library that only catches plain text profanity is trivial to bypass. This dataset tests the full evasion spectrum.

The dataset is in benchmarks/comparison/dataset.ts. PRs welcome.

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Results

Measured on darwin, Node.js v24.4.0. Absolute numbers vary by hardware — relative ranking is what matters.

1. Overall Accuracy

"How many mistakes does each library make?"

Library	Precision	Recall	F1	FPR	Accuracy	Total Errors
terlik.js	100.0%	68.1%	81.0%	0.0%	83.5%	211
obscenity	83.8%	28.9%	42.9%	6.0%	60.3%	508
bad-words	100.0%	19.2%	32.2%	0.0%	58.2%	535
allprofanity	89.7%	17.1%	28.7%	2.1%	56.1%	562

What do these metrics mean?

Metric	Plain English	Why it matters
Precision	"When it flags something, is it actually profane?"	Low precision = innocent messages get blocked. Users get frustrated.
Recall	"Does it catch all the profanity?"	Low recall = profanity slips through. The filter feels useless.
F1	Combined score (harmonic mean of Precision & Recall)	Single number to compare overall quality. 100% = perfect.
FPR	"How often does it wrongly flag clean text?"	Even 1% FPR means 1 in 100 clean messages gets blocked.

The bottom line: terlik.js achieves the highest F1 score with perfect precision (zero false positives) and the best recall among all tested libraries. On the curated 290-sample subset, terlik.js achieves 100% on all metrics.

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2. Category Breakdown — Where Each Library Succeeds and Fails

"What types of profanity does each library catch?"

Category	terlik.js	bad-words	obscenity	allprofanity	What it means
plain	50/50	43/50	42/50	37/50	Basic profanity like "fuck you"
variant	35/35	13/35	26/35	6/35	Compound forms like "shitfaced"
leet	25/25	13/25	20/25	14/25	Character tricks like "8itch"
separator	15/15	1/15	0/15	0/15	Spaced out like "f.u.c.k"
repetition	15/15	0/15	10/15	0/15	Stretched like "fuuuuck"
combined	15/15	4/15	10/15	6/15	Multiple tricks at once
clean	50/50	50/50	50/50	50/50	Normal text (should pass)
whitelist	70/70	70/70	67/70	70/70	Tricky words (should pass)
edge_case	15/15	14/15	14/15	14/15	Boundary conditions

The same data as percentages:

Category	terlik.js	bad-words	obscenity	allprofanity
plain	100%	86%	84%	74%
variant	100%	37%	74%	17%
leet	100%	52%	80%	56%
separator	100%	7%	0%	0%
repetition	100%	0%	67%	0%
combined	100%	27%	67%	40%
clean	100%	100%	100%	100%
whitelist	100%	100%	96%	100%
edge_case	100%	93%	93%	93%

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3. Evasion Detection Deep Dive

"Users actively try to bypass profanity filters. Which library survives?"

This is where the gap becomes dramatic. Evasion techniques are how real users bypass filters in chat, comments, and forums.

Evasion technique	Example	terlik.js	bad-words	obscenity	allprofanity
Separator dots	f.u.c.k	Caught	Missed	Missed	Missed
Separator spaces	s h i t	Caught	Missed	Missed	Missed
Separator dashes	a-s-s-h-o-l-e	Caught	Missed	Missed	Missed
Separator underscores	c_u_n_t	Caught	Missed	Missed	Missed
Char repetition	fuuuuck	Caught	Missed	Caught	Missed
Char repetition	sluuuut	Caught	Missed	Caught	Missed
Leet: numbers	sh1t, b1tch	Caught	Caught	Caught	Caught
Leet: symbols	$hit, a$$hole	Caught	Missed	Caught	Caught
Leet: 8→b	8itch	Caught	Missed	Missed	Caught
Leet: 6→g	ni66er	Caught	Missed	Caught	Missed
Leet: #→h	s#it	Caught	Missed	Missed	Caught
Phonetic: ph→f	phuck	Caught	Missed	Missed	Missed
Combined	phuck3d	Caught	Missed	Missed	Missed
Combined	n!66er	Caught	Missed	Caught	Missed
Combined	5h!tty	Caught	Missed	Caught	Caught

Summary: evasion detection rate

	terlik.js	bad-words	obscenity	allprofanity
Evasion samples caught	70/70	18/70	40/70	20/70
Evasion detection rate	100%	26%	57%	29%

What does this mean? If a user types "phuck you" or "f.u.c.k off", only terlik.js will catch it. The other libraries miss 43-74% of evasion attempts.

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4. False Positive Analysis

"Does it incorrectly block innocent words?"

A filter that blocks "analysis" because it contains "anal", or "grape" because it contains "rape", is worse than no filter at all — it erodes user trust.

Library	False Positives	Wrongly blocked words
terlik.js	0	None
bad-words	0	None
allprofanity	13	"spunky on sale now", "quality cockney for sale", "homogenous on sale now", "quality scunthorpe for sale", "Quality massage for sale", "trespass on sale now", "drape on sale now", "cockatiel on sale now", "quality shell for sale", "quality bassist for sale", "quality tycoon for sale", "quality piston for sale", "hello on sale now"
obscenity	37	"she moved to penistone last year", "the pussywillow bloomed in spring", "the pussycat slept all day", "We enjoyed the cockpit very much", "Latest pussyfoot technology", "cockpit how to guide", "my grandmother makes the best vandyke recipe", "everyone loves rapeseed", "Penistone wildlife is incredible", "my favorite vandyke recipe", "my neighbor recommended a great penistone brand", "pussywillow is out of stock", "shiitake is worth trying", "the book about shiitake was a bestseller", "volunteers organized a rapeseed drive for the holiday season", "my grandmother makes the best shiitake recipe", "Cockpit regulations updated", "the best rapeseed in town", "the best vandyke in town", "the annual rapeseed conference is held in September", "pussyfoot exam results are out", "I just discovered vandyke", "vandyke report is ready", "shiitake season has started", "I ordered rapeseed online", "I bought a pussywillow today", "We visited the penistone factory during our trip", "Penistone has become more affordable in recent years", "the children learned about rapeseed during their field trip", "She received an award for her contributions to rapeseed r...", "our teacher explained pussyfoot in great detail", "penistone festival happening this weekend", "I watched a tutorial on how to make vandyke at home", "studying pussycat at university", "the pussyfoot festival attracted thousands of visitors", "studying rapeseed at university", "rapeseed park is worth visiting"

How does terlik.js avoid false positives with 138 roots? A 105-entry whitelist explicitly protects words like:

Tricky word	Contains	Why it's safe
analysis	anal	Academic term
therapist	rape	Medical profession
grape	rape	Fruit
hello	hell	Common greeting
title	tit	Document element
pussywillow	pussy	Plant species
screwdriver	screw	Tool
cocktail	cock	Beverage
penistone	penis	English town name
assassin	ass	Historical term
cucumber	cum	Vegetable

All 70 whitelist trap sentences in the dataset pass correctly.

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5. Error Breakdown by Library

"What exactly does each library get wrong?"

terlik.js — 211 errors (0 FP, 211 FN)

Error type	Count	Examples

bad-words — 535 errors (0 FP, 535 FN)

Error type	Count	Examples
Missed plain profanity	7	"that is bullshit", "they had an orgy", "that hooker on the corner" and 4 more
Missed variant forms	22	"she is a bitchy person", "stop being an asshat", "you dipshit" and 19 more
Missed leet speak	12	"f#ck you", "a$$hole right here", "f@ck off" and 9 more
Missed separators	14	"f.u.c.k you", "s h i t man", "a-s-s-h-o-l-e" and 11 more
Missed repetition	15	"fuuuuck this", "shiiiit dude", "what an assshole" and 12 more
Missed combined	11	"$h!t posting", "f#cking idiot", "b.1.t.c.h mode" and 8 more
Missed edge cases	1	"RaPe MiXeD cAsE"

bad-words uses a simple word list. It can't handle any form of evasion — not leet speak, not separators, not repetition. Over half of all profane messages slip through.

obscenity — 508 errors (37 FP, 471 FN)

Error type	Count	Examples
False positives	37	"she moved to penistone last year", "the pussywillow bloomed in spring", "the pussycat slept all day" and 34 more
Missed plain profanity	8	"what the hell is this", "damn it all", "go to hell" and 5 more
Missed variant forms	9	"bullshitting everyone", "she is a dumbass", "jackass move dude" and 6 more
Missed leet speak	5	"phuck you", "8itch slap", "s#it stain" and 2 more
Missed separators	15	"f.u.c.k you", "s h i t man", "a-s-s-h-o-l-e" and 12 more
Missed repetition	5	"daaaamn son", "craaap quality", "helllll no" and 2 more
Missed combined	5	"F.U.C.K.I.N.G hell", "b.1.t.c.h mode", "phuck1ng loser" and 2 more
Missed edge cases	1	"embedded hell in the middle of a long clean sentence abou..."

obscenity is the strongest competitor. It handles some leet speak and repetition via transformer pipeline, but has no separator detection at all and generates false positives due to lack of whitelist.

allprofanity — 562 errors (13 FP, 549 FN)

Error type	Count	Examples
False positives	13	"spunky on sale now", "quality cockney for sale", "homogenous on sale now" and 10 more
Missed plain profanity	13	"what the hell is this", "damn it all", "go to hell" and 10 more
Missed variant forms	29	"she is a bitchy person", "stop being an asshat", "you dipshit" and 26 more
Missed leet speak	11	"f#ck you", "f@ck off", "what a pr1ck" and 8 more
Missed separators	15	"f.u.c.k you", "s h i t man", "a-s-s-h-o-l-e" and 12 more
Missed repetition	15	"fuuuuck this", "shiiiit dude", "what an assshole" and 12 more
Missed combined	9	"F.U.C.K.I.N.G hell", "f#cking idiot", "b.1.t.c.h mode" and 6 more
Missed edge cases	1	"embedded hell in the middle of a long clean sentence abou..."

allprofanity's Aho-Corasick engine is fast but rigid. It matches exact strings from its word list and can't handle any morphological or evasion variation.

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6. Throughput

"How fast can each library process messages?"

Measured in operations per second (higher = better). Each operation processes a batch of 100 messages.

Detection speed (check/containsProfanity):

Library	ops/sec	avg latency	p50	p95	p99	vs terlik.js
obscenity	70,309	1,422 μs	1,410 μs	1,496 μs	1,593 μs	~1.92x faster
allprofanity	47,131	2,122 μs	2,106 μs	2,209 μs	2,294 μs	~1.29x faster
terlik.js	36,627	2,729 μs	2,723 μs	2,835 μs	2,960 μs	—
bad-words	3,076	32,510 μs	32,418 μs	33,093 μs	34,177 μs	11.9x slower

Cleaning speed (clean/censor):

Library	ops/sec	avg latency	p50	p95	p99	vs terlik.js
obscenity	48,481	2,063 μs	2,049 μs	2,146 μs	2,252 μs	~1.34x faster
allprofanity	47,278	2,115 μs	2,105 μs	2,182 μs	2,227 μs	~1.30x faster
terlik.js	36,260	2,757 μs	2,746 μs	2,888 μs	3,093 μs	—
bad-words	607	164,826 μs	164,422 μs	168,109 μs	171,898 μs	59.7x slower

What do these numbers mean in practice? At 37K ops/sec, terlik.js can check ~36,627 messages per second on a single core. For a chat app with 1,000 concurrent users sending 1 message/second, that's 37x headroom. obscenity edges ahead on raw check() by ~92%, but terlik.js is -25% faster on clean() and catches 30% more profanity (100% vs 70% recall). bad-words would need 12 cores for the same load.

p95/p99 explained: p95 = 95% of batches complete within this time. p99 = 99%. Low p99 means consistent performance without random spikes.

Where Does the Throughput Go?

terlik.js runs a multi-pass detection pipeline that trades raw speed for detection depth. Here's what each layer costs and what it catches:

Detection Layer	Throughput Cost	What It Catches	Can You Disable It?
NFKD decomposition	~3%	Fullwidth evasion (ｆｕｃｋ), accented tricks (fück)	No — safety layer
Cyrillic confusable mapping	~1%	Cyrillic а→a, о→o substitution	No — safety layer
Combining mark stripping	~1%	Diacritics used for evasion (s̈h̊ït)	No — safety layer
Locale-aware lowercase	~1%	İ→i, I→ı (Turkish locale correctness)	No — safety layer
Leet decode + number expansion	~5%	$1kt1r, f#ck, 8itch, s2k	Yes: disableLeetDecode: true
CamelCase decompounding	~3%	ShitLord, FuckYou (non-variant compounds)	Yes: disableCompound: true
Suffix chain matching	~3%	Suffixed forms (siktirci, fuckers)	No — core detection

Do NOT disable safety layers for performance. The ~6% total cost of NFKD + Cyrillic + diacritics + locale lowercase is non-negotiable — without them, trivial Unicode tricks bypass the entire filter. These layers run on every input regardless of toggle settings.

When to Use the Optional Toggles

Toggle	Use Case	Performance Gain	Risk
disableLeetDecode: true	Input is from a controlled source (e.g., CMS with restricted charset, database field names)	~5% faster	Misses $1kt1r, 8itch, f#ck, number-embedded evasions
disableCompound: true	Camel case is intentional (e.g., code identifiers like ShittyClassName)	~3% faster	Misses novel compounds not in dictionary (ShitLord, FuckBoy if not variant)
minSeverity: "medium"	Skip mild words (damn, crap, hell) in casual contexts	Marginal (pattern-level skip)	Low-severity profanity passes through
excludeCategories: ["sexual"]	Allow general/insult words but block sexual content	Marginal (pattern-level skip)	Selected category passes through

Important: disableLeetDecode does NOT disable all substitution detection. The regex charClass patterns (Pass 1) still catch some visual substitutions like $ → s. The toggle only disables the normalizer's decode stages (leet map + number expansion). If a user says "I set disableLeetDecode but $hit is still caught" — that's by design. The charClass patterns are part of the core regex engine, not the normalizer.

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7. Memory Usage

"How much RAM does each library consume?"

Heap and RSS delta measured in KB:

Library	Init Heap	Init RSS	After 2K msgs Heap	After 2K msgs RSS	Notes
terlik.js	+1,448 KB	+32 KB	+6,480 KB	+328 KB	GC reclaims memory efficiently
bad-words	+204 KB	+0 KB	-6,780 KB	+48 KB	Heap grows with usage
obscenity	+188 KB	+52 KB	+5,910 KB	+36 KB	Lightweight, stable
allprofanity	+1,319 KB	+124 KB	-18,951 KB	-28,076 KB	Large init (loads 2 dictionaries)

What does this mean?

• Init Heap: Memory used just to load the library. All are lightweight (~170-220 KB) except allprofanity (1.3 MB, loads English + Hindi dictionaries by default).
• After processing: terlik.js and allprofanity show negative heap deltas — V8's garbage collector reclaims memory after JIT optimization. bad-words accumulates ~9.5 MB after processing 2,000 messages.
• Run with node --expose-gc for precise measurements.

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8. Bundle Size

"How much does this add to my application?"

Library	Raw size	Gzipped	Dependencies
terlik.js	~67 KB	~14 KB	0
obscenity	~45 KB	~8 KB	0
bad-words	~15 KB	~3 KB	1 (badwords-list)
allprofanity	~80 KB	~15 KB	2

Dictionary expansion impact (v2.4.1 → current):

	v2.4.1	Current	Delta
English roots	36	138	+102
Explicit variants	139	342	+203
Whitelist entries	60	105	+45
Suffix rules	8	90	+82
ESM raw	~57 KB	~67 KB	+10 KB
ESM gzip	~12 KB	~14 KB	+2 KB

The English dictionary expansion (102 new roots, 203 variants, 45 whitelist entries, 82 suffix rules) plus multi-pass normalization features added only +2 KB gzipped.

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9. The Trade-off Map

"Every library makes trade-offs. Here's where each one sits."

	High Precision	High Recall	Fast	Small	Evasion-proof
terlik.js	Yes	No	Yes	Yes	Yes
obscenity	No	No	Yes	Yes	Partially
bad-words	Yes	No	No	Yes	No
allprofanity	No	No	Yes	Yes	No

In plain English:

• terlik.js — Highest F1, perfect precision, zero false positives, competitive speed, zero dependencies. The only library that handles all evasion types. Multi-pass detection pipeline trades raw speed for detection depth.
• obscenity — Decent leet handling, fastest raw throughput. Can't handle separators and has no whitelist (causes false positives). Closest competitor on speed, distant on accuracy.
• bad-words — Zero false positives, but misses the majority of profanity. Any determined user will bypass it. Significantly slower than all competitors.
• allprofanity — Similar to bad-words but with more language support. Weak evasion handling. Fast init but large memory footprint.

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How terlik.js Achieves This

The detection pipeline explains why terlik.js catches what others miss:

Input: "phuck y0u m0th3rfuck3r"
  │
  ├─ Pass 0: 10-stage normalizer
  │   ├─ 1. Strip invisible chars (ZWSP, soft hyphen, etc.)
  │   ├─ 2. NFKD decompose (ｆｕｃｋ → fuck, accented → base)
  │   ├─ 3. Strip combining marks (diacritics)
  │   ├─ 4. Locale-aware lowercase
  │   ├─ 5. Cyrillic confusable → Latin (Cyrillic а→a, о→o, etc.)
  │   ├─ 6. Char folding (ü→u, ö→o, etc.)
  │   ├─ 7. Number expansion (s2k → sikik in TR)
  │   ├─ 8. Leet decode (0→o, 3→e, $→s)
  │   ├─ 9. Punctuation removal (f.u.c.k → fuck)
  │   └─ 10. Repeat collapse (fuuuck → fuck)
  │   Result: "phuck you motherfucker"
  │
  ├─ Pass 1: Locale-lowered regex matching
  │   charClasses: f=[fph], so "ph" → matches "f" pattern
  │   "phuck" matches /[fph][uv][c][k]/ → ✅ fuck
  │
  ├─ Pass 2: Normalized text regex matching
  │   "motherfucker" → ✅ direct variant match
  │
  ├─ Pass 3: CamelCase decompounding (ShitLord → Shit Lord)
  │
  ├─ Whitelist check → neither word is whitelisted
  └─ Result: 2 matches found

Other libraries fail at different stages:

• bad-words has no leet decode, no separator removal, no pattern matching
• obscenity has leet decode + some patterns, but no separator removal and no phonetic matching (ph→f)
• allprofanity has basic matching but no normalization pipeline

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Transparency: Where terlik.js Can Still Improve

Leading on 1280 samples doesn't mean perfection in the wild. Known limitations:

Limitation	Example	Status
Context-dependent words	"hell yeah" is flagged (but user may consider it mild)	By design — severity: "low"
3-letter root FP risk	"cum" in "cucumber"	Protected by whitelist, but new compounds may appear
Rapidly evolving slang	New coinages not in dictionary	Use addWords() or customList at runtime
Non-Latin scripts	Cyrillic "а" substituted for Latin "a"	Handled (Cyrillic confusable → Latin mapping)
Adversarial transforms	Zalgo, zero-width chars, unicode homoglyphs	Partially handled — some extreme evasions reduce recall
Dataset composition	290 curated + 990 SPDG-generated adversarial samples	Continuously expanding via SPDG

We document every limitation and encourage the community to report edge cases.

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Methodology

Parameter	Value
Warmup iterations	100 (V8 JIT compilation)
Measurement iterations	1,000
Batch size	100 messages per iteration
Latency metric	Per-batch microsecond timing
Percentiles	p50, p95, p99
Memory measurement	process.memoryUsage() delta
Isolation	Each library in independent adapter
Fairness	Same corpus, same machine, same process

Adapter Pattern

Each library is wrapped in a LibraryAdapter interface with init(), check(), and clean() methods:

Library	check() API	clean() API
terlik.js	containsProfanity()	clean()
bad-words	isProfane() / clean() !== text	clean()
obscenity	matcher.hasMatch()	censor.applyTo()
allprofanity	check()	clean()

Adapters: benchmarks/comparison/adapters.ts

Limitations of This Benchmark

Limitation	Impact
English only	Doesn't test TR/ES/DE — only the common denominator
1280 samples	290 curated + 990 SPDG adversarial. Comprehensive but not exhaustive
Self-reported	Maintained by terlik.js team. We aim for fairness but acknowledge potential bias
Single machine	Absolute ops/sec numbers depend on hardware. Relative ranking matters more
Default configs	All libraries tested with default settings. Custom tuning may improve results
bad-words v3	Tested v3.0.4 (latest on npm). A v4 may exist but npm resolves to v3

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Run It Yourself

Prerequisites: Node.js >= 18, pnpm

# From the repository root:
pnpm build                  # Build terlik.js first
pnpm bench:report           # Build + install deps + run benchmark + generate this report

# Or manually:
cd benchmarks/comparison
pnpm install
npx tsx run.ts              # Standard run
node --expose-gc --import tsx/esm run.ts   # With accurate memory

Output:

1. Formatted tables to the console
2. JSON results to benchmarks/comparison/results/comparison-results.json
3. This markdown report to docs/benchmark-comparison.md

Modifying the Benchmark

Task	How
Add test cases	Edit dataset.ts — add { text, profane, category }
Add a library	Create adapter in adapters.ts, add factory to run.ts
Change iterations	Edit WARMUP_ITERATIONS / MEASURE_ITERATIONS in throughput.ts

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Raw Data

Full JSON output with per-sample error lists: benchmarks/comparison/results/comparison-results.json

Contributing

Found an issue with the methodology? Think the dataset is unfair? Please open an issue or PR. We want this comparison to be as fair and transparent as possible.