NLP Content Analyzer Benchmarks

Every modern search and answer system runs the page through the same basic machinery first: segment it, find the entities, work out the intent. This tool reports what that machinery sees. It is a diagnostic rather than a scorecard, and its usefulness is in explaining results the other tools only describe — a page that keeps scoring badly for reasons nobody can name usually turns out to be hard to parse in some specific, fixable way.

Here is where that sits. The median is 71/100, which ranks it 9 of the 25 measures in this dataset. The middle half of results falls between 50 and 74 — a band 24 points wide. That is wide: sites genuinely diverge here, which is to say getting ahead is still possible.

How the scores are distributed

An average on its own misleads. The chart below splits all 997 scans into five score bands, totalling 100%. Most results sit in the middle bands, which means sites differ here by degree rather than by whether they bothered at all.

• 0–19
• 20–39
• 40–59
• 60–79
• 80–100

NLP Content Analyzern = 997

27%51%15%

The grade mix

The same data as letters: A+ 95–100, A 85–94, B 75–84, C 65–74, D 50–64 and F below 50.

Where your score stands

Read this as a legibility measure, not a quality one. A high score means the processing layer sees your page clearly: the sentences resolve, the entities are identifiable, the structure signals intent. It says nothing about whether what it sees is any good — but a page the machinery cannot parse cleanly will underperform regardless of how good it is.

NLP Content Analyzermedian 71

0255075100

How to read it: 50 is bottom-quartile, 71 is a typical page, 74 starts the upper quartile and 85 or above is the top 10%. These thresholds apply to this tool only.

The checks that fail most

Three things dominate the failures. Sentences that run on, so the parser loses the subject. Missing named entities, so nothing anchors the page to a known thing in the world. And an absence of concrete facts, so there is nothing to extract even once the text is understood. They are separate problems with one shared symptom: the page is about something in a way no machine can pin down.

“Hard fail” means the check’s full weighting is lost; “partial” means only part of the score is. Rates are the share of scans in which the check ran and did not pass.

The weakest areas

This tool groups its checks into categories. The chart below shows how often each category was the lowest-scoring one in a scan — which area is most often the weak link. It is not the average score in that category; it is how often it came last.

“Structure & Intent” was the weakest area in 60% of scans.

What to fix first

Name things explicitly instead of referring back to them. Pronouns and "it" chains are the single most common cause of low scores here, because they force the parser to resolve a reference that may be several sentences away. Repeating a proper noun where you would normally write "it" reads slightly more formally and parses enormously better.

A typical scan passes 4.4 of 7 checks and leaves 2.6 open items behind. What drags a score down is usually not one catastrophe but accumulated small omissions.

Run the NLP Content Analyzer on your own page

Month by month

The figures on this page are replaced at every refresh. At the end of each month a permanent edition is published for that month and never edited again — which is the only reason this series can be compared at all. One edition has been published so far; when the second lands this section will set the two months side by side.

71August 2026

Frequently asked questions

What is a named entity and why does it matter?

It is a specific thing a system can identify and connect to what it already knows: a company, a person, a place, a product, a standard. Entities are how a page gets attached to a subject rather than merely containing words about one. A page with no identifiable entities is topically vague to a machine even when it is perfectly clear to a reader.

Does this measure keyword density?

No, and deliberately not — keyword density has not been a useful signal for a very long time. What it looks at is whether the page reads as being about an identifiable subject: which entities appear, whether they recur naturally, whether the surrounding vocabulary supports them. Repeating a phrase to hit a ratio works against that rather than for it.

How does this relate to search intent?

Intent is inferred largely from structure and phrasing — questions, comparisons, instructions, definitions each look different to a parser. A page whose structure does not signal any particular intent is harder to match to a query, which is why the check looks at whether questions are asked and whether the page states things plainly.

What is a good score on the NLP Content Analyzer?

In this dataset the median is 71/100, so scoring 71 puts you exactly in the middle. 74 is the start of the upper quartile and 85 or above is the top 10%; below 50 is the bottom quarter. Those thresholds are specific to this tool — the 25 measures in this dataset have very different medians, so 70 here and 70 elsewhere do not mean the same thing.

Which check fails most often on the NLP Content Analyzer?

“Readable prose”, which did not pass in 83% of scans. The costliest failures, though, are the ones scored as hard fails — this tool has 2, the most common being “Digestible sentence length” at 26%.

Questions that govern the whole series — how the sample is collected, how sites are anonymized, how often the figures are refreshed — are answered in the methodology on the hub.

Using this data

The figures, tables and charts on this page are free to reuse under CC BY 4.0; the only condition is a link back to this page. Suggested citation: SeoMods — NLP Content Analyzer benchmarks, 2 September 2026, https://seomods.com/seo-score-benchmarks/nlp-analyzer. If you need to cite by date, use the August 2026 edition instead: this page changes at every refresh and that one never does.