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Text Readability Score API

POST

Know the reading grade level and target audience of any text.

Text Readability Score calculates the readability score and grade level of any text across standard formulas. It returns Flesch Reading Ease, Gunning Fog, Coleman-Liau, syllable and word counts, an average grade level, and a target audience label.

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Request
POSTapi.apiverve.com/v1/readabilityscore
Body
Verification
Format

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Example
{
  "status": "ok",
  "error": null,
  "data": {
    "textCounts": {
      "syllableCount": 82,
      "lexiconCount": 49,
      "sentenceCount": 1
    },
    "readability": {
      "fleschReadingEase": 13.28,
      "fleschKincaidGrade": 23.6,
      "gunningFog": 26.95,
      "colemanLiauIndex": 13.19,
      "smogIndex": 22.08,
      "automatedReadabilityIndex": 27.8,
      "daleChallReadabilityScore": 17
    },
    "readabilityText": {
      "fleschReadingEase": "very confusing",
      "daleChallReadabilityScore": "average 13th to 15th-grade (college) student"
    },
    "averageGradeLevel": 22.7,
    "targetAudience": "post-graduate"
  }
}

About the Text Readability Score API

A readability score API that grades how hard a piece of text is to read. Readability Score works by analyzing the text provided and calculating the readability score with seven established readability formulas, including the Flesch Reading Ease score, Flesch Kincaid grade level, Gunning Fog index and Dale Chall readability, in a single call.

Alongside the raw scores you get plain-language labels such as "fairly difficult", an averaged reading grade level and a suggested target audience from elementary to post-graduate, plus the syllable, word and sentence counts behind the numbers. Content teams use it to check articles against a house reading level, publishers and educators use it to match texts to readers, and product and legal teams use it to keep help pages, notices and consent text readable.

Every field is returned on every plan, including the Free plan, so you can build a full readability report or a simple pass and fail check from the same response.

How it works

Readability Score first replaces every tab, carriage return and line feed in text with a space and trims it; empty text returns a 400 error. Because line breaks become spaces, a heading or bullet without terminal punctuation runs into the next sentence, which raises the sentence-length based scores. Add periods to list items if you want them counted as separate sentences.

textCounts.syllableCount, lexiconCount (words) and sentenceCount come from the text-readability library, as do Flesch Reading Ease, Flesch-Kincaid Grade, Gunning Fog, Coleman-Liau, the Automated Readability Index and the New Dale-Chall score. smogIndex is taken from the readability-scores library instead. These are standard formulas over syllables per word, words per sentence, characters per word, polysyllabic word counts and, for Dale-Chall, the share of words outside a list of familiar words.

All grade-style metrics and the Dale-Chall score are floored at 1. fleschReadingEase is not floored: it runs roughly 0 to 100 with higher meaning easier, and very dense text can go below 0. Its label in readabilityText uses lower bounds of 90 very easy, 80 easy, 70 fairly easy, 60 standard, 50 fairly difficult, 30 difficult, and very confusing below that. The Dale-Chall label maps whole-number bands from "average 4th-grade student or lower" up to "13th to 15th-grade (college) student" for 9 and above.

averageGradeLevel is the mean of Flesch-Kincaid, Gunning Fog, Coleman-Liau, SMOG and ARI, rounded to one decimal. targetAudience reads from it: 16 and above post-graduate, 13 college, 9 high school, 6 middle school, otherwise elementary. Short samples of a sentence or two produce unstable scores. Results are identical on every plan.

What people use it for

Editorial CMS Publishing
Before publishing articles, newsroom CMS editors check the Flesch-Kincaid grade level and word counts to match target reader expectations.
Patient Instructions Review
When clinicians draft care summaries, health portals flag passages scoring above an elementary target audience so staff can rephrase dense medical terms.
Curriculum Reading Assessment
To assign books to suitable grade bands, edtech apps evaluate uploaded chapters against Dale-Chall scores and the average grade level.
Consumer Contract Auditing
Flag convoluted policy clauses during drafting by checking consumer agreements against the Gunning Fog index and reading ease ratings.

Ways to call it

One endpoint, many ways in — REST with JSON, XML, YAML and CSV, plus GraphQL and an MCP interface for AI agents.

JSON
Default REST response
XML
Markup format
YAML
Human-readable
CSV
Tabular export
Beta
GraphQL
Query language
New
MCP
For AI agents

Other ways to use Text Readability Score

Same data, same APIVerve account, same credit balance — one key works on all of them.

Questions.

Common questions about the Text Readability Score API.

Read the docs →
Which readability formulas are included in the analysis?
The API calculates scores across seven standard formulas: Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog, Coleman-Liau Index, SMOG Index, Automated Readability Index, and the New Dale-Chall score. Each metric evaluates text difficulty using distinct structural or vocabulary rules.
Does it return plain-language ratings alongside the raw scores?
Yes. In addition to numeric values, you receive text interpretations such as difficulty labels derived from Flesch Reading Ease and Dale-Chall. It also assigns a target audience classification, such as elementary, high school, or post-graduate.
Does the response break down word and sentence counts?
Yes. Every response includes total syllable counts, word counts, and sentence counts detected in the analyzed text. These structural metrics show the exact quantities used by the readability formulas.
How is the overall grade level calculated?
The API computes an average grade level by averaging the grade-based metrics, including Flesch-Kincaid, Gunning Fog, Coleman-Liau, and SMOG. This gives you a single composite school-grade benchmark without requiring you to average the individual formulas manually.
Which plan fits analyzing 50,000 texts a month?
Starter easily covers that volume. Each analysis uses 2 credits, so 50,000 texts consume 100,000 credits, using half of Starter's 200,000 monthly credits. That volume works out to about $0.30 per 1,000 calls. The Free plan includes 100 calls per month.

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