Readability score
Grade your writing with the Flesch Reading Ease, Flesch-Kincaid, Gunning-Fog and SMOG indices. Runs in your browser; nothing is uploaded.
About this tool
Readability score grades how hard your text is to read using the formulas editors, teachers and content teams rely on:
- Flesch Reading Ease — a 0–100 score where higher means easier (60+ is considered plain English).
- Flesch-Kincaid Grade Level — the US school grade needed to read the text.
- Gunning-Fog Index — years of formal education a first reading should require, weighted by "complex" (3+ syllable) words.
- SMOG Index — a grade level built from polysyllabic word counts, popular for health and safety copy.
- Coleman-Liau Index — a grade level based on letters per word and sentences per word (no syllable estimate, so it sidesteps syllable-counting error).
- Automated Readability Index (ARI) — a grade level from characters per word and words per sentence.
It also shows the raw counts behind the scores — words, sentences, syllables and complex words — plus average words per sentence and syllables per word, so you can see why a passage scores the way it does.
Privacy
Everything runs in your browser via WebAssembly — your text is never uploaded to a server. You can also run it from the gizza CLI or inside a gizza chat, which return the same indices as structured JSON.
Common uses
- Aim an article, email or landing page at a target reading level.
- Simplify dense paragraphs until the grade level drops.
- Compare drafts to see which reads more easily.
Notes
The syllable count uses an English heuristic (vowel groups with silent-e and
-le adjustments), so scores are estimates — accurate enough to compare drafts
and hit a target band, but they can differ by a fraction of a grade from a
dictionary-based counter. SMOG is designed for samples of about 30 sentences.
FAQ
Which readability formulas does it calculate?
Six at once: Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning-Fog, SMOG, Coleman-Liau and the Automated Readability Index (ARI) — plus their average grade and a plain-language reading level. It also shows the raw counts (words, sentences, syllables, complex words) so you can see what drives each number.
Why do the different indices disagree on a grade?
Each formula weights different signals. Flesch-Kincaid, Gunning-Fog and SMOG lean on syllable and complex-word counts, while Coleman-Liau and ARI use letters/characters per word instead — so they sidestep syllable-counting error but react differently to long words. A spread of a grade or two across the six is normal; the average smooths it out.
Is the English scoring exact, and does it work for one sentence?
Syllables are estimated with an English heuristic, so grades are close but can differ by a fraction from a dictionary counter — great for comparing drafts, not for a legal cutoff. It scores any length, but SMOG is designed for ~30 sentence samples, so single-sentence SMOG values are unreliable. Empty input returns "No text to analyze." rather than a score.
Is my text sent anywhere?
No — every count and score is computed in your browser with WebAssembly, so unpublished drafts and private documents never leave your device.
Developer & Automation Access
Run it from the terminal
Same engine as this page, headless — via the gizza CLI:
gizza tool readability-score "Paste or type the text you want to score…"New to the CLI? Get gizza →
Open it by URL
Pre-fill and auto-run this tool with query parameters — the names match the API/CLI:
https://gizza.ai/tools/readability-score/?text=Paste%20or%20type%20the%20text%20you%20want%20to%20score%E2%80%A6Machine-readable descriptor: tool.json — title + parameters JSON Schema for agents.