AI Style Analyzer
Check terminology, spelling, punctuation and voice against your preferred style. Try the same tool available in your dashboard, then carry your input, options and result into your account.
Check terminology, spelling, punctuation and voice against your preferred style.
Add at least 60 words to your document.
One thing called three names, capitals that will not settle, numbers spelled two ways — each with the competing forms quoted and the one that should win.
Five things that stop agreeing with themselves.
Roughly in order of how much damage each does, which is not the order people usually worry about them in.
- One thing called three namesThe most damaging inconsistency and the least visible. A document that says user, customer and client for the same person, or sign in, log in and login across three sections, reads as though nobody was in charge of it. In technical and product writing it is worse than untidy — a reader reasonably assumes different words mean different things.
- Capitalisation that will not settleFeature names capitalised in one paragraph and lowercase in the next, headings switching between title case and sentence case, job titles capitalised inconsistently. Individually trivial, collectively the clearest signal that a document was assembled rather than written.
- Numbers, dates and units in competing formatsTen and 10 in adjacent sentences, 5% and five per cent, 2026-08-31 against 31 August 2026, mixed metric and imperial. Most style guides set a threshold — spell out under ten, numerals above — and the specific rule matters far less than picking one and holding it.
- Serial commas, dashes and quotation marksThe serial comma is the famous one and the least consequential provided you are consistent. More visible in practice: straight quotes mixed with curly ones, hyphens standing in for dashes, and spaced versus unspaced em dashes. These are exactly what a document accumulates when text is pasted in from several sources.
- Person and voice changing mid-documentA guide that opens in second person, drifts into first person plural, and finishes with impersonal constructions. Readers rarely name this and reliably feel it, because each shift quietly changes who is being addressed.
Each session starts from nothing, and that is the whole problem.
Style inconsistency in AI-assisted documents is not random sloppiness. It has a specific cause and therefore a specific place it shows up, which makes it easy to find once you know where to look.
A model carries nothing between sessions. It does not know that three weeks ago you settled on “customer” rather than “user”, or that headings in this document are sentence case. Each draft picks a sensible default from scratch, and sensible defaults are not the same default twice.
So the inconsistencies cluster at section boundaries. Each section is internally consistent, because it was written in one pass. The document is not, because the passes did not know about each other. This is the opposite of how single-author human writing drifts, which is gradual and spread out.
Which is exactly what a re-read misses. Reading your own document end to end, attention is at its lowest precisely at the joins, and comparing a term on page two against the same term on page nine is not something reading does at all. It is a search problem rather than a reading problem.
And it reads as assembled. This is the cost worth caring about. A reader may never identify why, and they register that the document was put together from pieces rather than written. In documentation it goes further than impression: two names for one thing is a genuine ambiguity about whether there are two things.
The practical habit that helps most is keeping a short list of the terms and conventions this document uses, and pasting it into each new session. Twenty lines prevents most of this.
How ChatGPT, Claude, Gemini, DeepSeek, Grok, LLaMA and Perplexity drift.
Each is inconsistent in a characteristic direction, which tells you what to search for first.
ChatGPT style analyzer
Extremely consistent within a response and not at all across them.
Within one output ChatGPT holds terminology and formatting well. Across separate sessions it holds nothing, because there is no memory of the decisions the last draft made. A document written over five sittings will use different terms for the same concept in different sections, and each section will look internally fine.
This is the single best argument for checking style rather than reading for it. The inconsistencies sit at section boundaries, which is exactly where a re-read loses attention.
Claude style analyzer
The most consistent prose of the assistants, with one recurring formatting problem.
Claude maintains terminology and register well within a long output, better than the others. Where it introduces inconsistency is structural: Markdown emphasis and bulleted breakdowns arriving in some sections and not others, so a document ends up half formatted.
Pasted into a system that does not render Markdown, that becomes literal asterisks in some paragraphs and clean prose in others, which is the most visible kind of inconsistency there is.
Gemini style analyzer
Structural inconsistency is the default rather than the exception.
It switches between prose and bulleted lists unpredictably, so a document contains sections that argue and sections that enumerate with no principle distinguishing them. That is a style inconsistency before it is a formatting one: it changes how the reader is expected to read.
Heading case also drifts, since it generates headings freely and does not track what case the earlier ones used.
DeepSeek style analyzer
Terminology is the thing to check, and it is checkable.
Technical output uses precise vocabulary, and precise vocabulary is exactly where naming one concept two ways does real harm. A reader who meets two terms reasonably infers two things, which in documentation produces a genuine misunderstanding rather than an aesthetic complaint.
Its register is otherwise stable, so a DeepSeek document tends to be consistent in tone and inconsistent in nomenclature.
Grok style analyzer
Register is the inconsistency here rather than mechanics.
Grok modulates informality unpredictably — a passage with asides and jokes followed by a straight one — so a document swings in voice without swinging in vocabulary. That reads as an author who could not decide how serious to be.
Text copied from X also brings that platform's spacing and short standalone lines, which sit inconsistently against paragraphs written elsewhere.
LLaMA style analyzer
The widest internal variance of any assistant, on every dimension at once.
LLaMA is weights rather than a product, so a document assembled from several applications running the same model name can differ in terminology, register and formatting between sections. This is the case a style check is most useful for, because there is no consistent baseline to read against.
Smaller deployments also spell the same proper noun two ways within one document, which is worth searching for directly.
Perplexity style analyzer
It inherits the style of its sources, which is the specific problem.
Because it writes from retrieved material, its terminology follows whatever the sources used. Research pulled from documents in two traditions produces a draft that uses both vocabularies, and the inconsistency arrived from outside rather than from the model drifting.
Citation formatting is the other one: reference styles vary between what it returns, so a document can carry two conventions. Both are worth normalising deliberately rather than leaving to a pass.
You probably need twenty lines, not a published manual.
Teams that hit this problem tend to reach for AP or Chicago, and that is usually more than the problem requires and less than it needs.
A published guide answers questions nobody in your team is asking. Chicago is a book. Almost none of it concerns the twenty decisions you actually keep getting wrong, and the length means nobody consults it, which leaves you with a guide and the same inconsistencies.
What works is the list of your own recurring decisions. The product names and their exact capitalisation. Whether it is sign in or log in. Customer or user. Sentence case or title case for headings. Serial comma, yes or no. Date format. That is a page, everybody reads it, and it prevents most of what this checker will find.
The specific choices matter far less than holding them. The serial comma is the standard illustration: Chicago requires it, AP largely does not, and no reader has ever been confused by a document that simply picked one. The confusion comes entirely from switching.
And paste that list into every AI session. Since the model carries nothing between sittings, handing it your conventions at the start of each one is the cheapest fix available for the cause described above.
Style analyzer questions.
What does an AI style analyzer check?
Whether a document is consistent with itself: one term for one thing, capitalisation that holds, numbers and dates in a single format, punctuation conventions applied throughout, and a stable person and voice. It is a different question from whether the writing is good — a document can be well written and still contradict itself between sections.
How is this different from a grammar checker?
A grammar checker judges each sentence against the rules of English. A style analyzer judges the document against itself. Writing “log in” in one section and “login” in another breaks no grammatical rule, and a reader still notices. Neither tool catches what the other is looking for.
Why does AI-assisted writing drift in style?
Because a model has no memory of the decisions the previous session made. Every draft starts from nothing and picks reasonable defaults, and reasonable defaults differ between sittings. A document written over five sessions can be internally consistent within each section and inconsistent across the document, which is exactly the pattern a re-read misses.
Which inconsistency matters most?
Terminology, by a distance. Calling one thing by three names is worse than any punctuation drift, because a reader reasonably assumes different words mean different things. In product and technical writing that produces real misunderstanding rather than an aesthetic complaint.
Do I need a style guide to use this?
No. Without one it reports inconsistency — where the document disagrees with itself — and lets you pick which version wins. With one it can check against your decisions. Most teams benefit more from a short list of the twenty terms they keep getting wrong than from adopting a full published guide.
Should I follow AP, Chicago or something else?
Whichever your field expects, and the specific choice matters far less than consistency. The serial comma is the standard example: Chicago requires it, AP mostly does not, and no reader has ever been confused by a document that simply picked one. The confusion comes from switching.
Can it check style in ChatGPT, Claude and Gemini text?
Yes, and the sections above cover each. In short: ChatGPT is consistent within a response and not across sessions, Claude holds terminology well but formats unevenly, Gemini switches between prose and bullets without a principle, and LLaMA varies most on every dimension at once.
Does style consistency affect AI detection?
Not directly, and inconsistency is a signal a human reader picks up. A document whose terminology and register change at section boundaries reads as assembled from several sources, which is often exactly what happened. That is more noticeable to an editor than any score.
Will it change my writing style?
No. It reports where the document disagrees with itself and leaves the decision to you, because which version is correct is a judgement about your subject and your audience. A tool that picked for you would be imposing a house style you never chose.
Is my text stored?
The document is attached to your account so you can return to it, and you can delete it whenever you like. It is not used to train anything.