Readability Analyser

A readability analyser measures how complex your text is to read, using established linguistic formulas that calculate sentence length and vocabulary density. This tool reports Flesch-Kincaid Grade Level — the most widely used readability standard — alongside passive voice percentage and transition word density. Together these give you a diagnostic picture of where your writing is clear and direct, and where it is likely to slow a reader down. The same properties that make text easier for humans to read also tend to make it easier for AI systems to parse and extract from, which is why readability analysis has become relevant to GEO alongside its traditional use in content and UX writing.

Flesch-Kincaid Grade Level tells you the US school grade at which your text becomes readable — Grade 8–10 is the established target for general audiences. Flesch Reading Ease expresses the same measurement on an inverse scale: higher is easier. The two scores together are the industry standard pair; a piece at Grade 9 and Flesch 65 is well-calibrated for most content use cases.

Complex word percentage shows whether a high grade level is driven by vocabulary or by sentence length. A document at Grade 12 with 8% complex words is fixable by splitting sentences. One with 28% complex words needs vocabulary simplification — a fundamentally different edit.

Long sentences lists the specific sentences driving your sentence length score upward. Aggregate averages can mask a few dense outliers — fixing three sentences can move the overall grade level by two points.

Readability affects how reliably AI systems can extract and attribute content. The Flesch-Kincaid Grade Level formula — developed by Kincaid et al. in 1975 and still the readability standard across publishing, healthcare, and education — measures average sentence length and syllable density to estimate reading complexity. Content at Grade 8–10 is consistent with practitioner GEO guidance and is broadly associated with clearer structural properties for AI parsing, though this connection is an informed inference rather than a measured finding. Active voice keeps the subject of each sentence unambiguous — important when AI systems need to attribute a claim to a named entity. Transition words support human reading flow and logical structure; however, sentences that begin with transitional openers tend to score lower on AI extractability because they depend on the preceding sentence for context. These are directional findings, not hard thresholds — treat them as signals, not rules.