AI Tone Analyzer
See how your writing may come across to its reader, with examples and practical suggestions. Try the same tool available in your dashboard, then carry your input, options and result into your account.
See how your writing may come across to its reader, with examples and practical suggestions.
Add at least 30 words to your text.
Warmth, confidence, formality, directness and urgency, with quoted examples and advice suited to your reader.
Five things tone is made of, and they move independently.
Reading them separately matters, because a message can be warm and unconfident, or direct and cold, and those need different fixes.
- Warmth — whether the reader feels addressedThe difference between writing to a person and writing about a matter. Warmth comes from second person, from acknowledging what the reader is dealing with, and from contractions. It is the dimension AI drafts most often miss, because a model does not know whether it is writing to a stranger or to someone you have worked with for years.
- Confidence — whether you commit to anythingHedging is measurable: might, could, generally, in most cases, it is worth considering. Some of it is honest precision and some is a reflex. The test is whether removing a qualifier would make the sentence false. If not, it was padding, and a paragraph full of it reads as having no view.
- Formality — register against relationshipNot a scale where one end is better. Formality signals distance, which is right for a legal notice and wrong for a note to a teammate. The failure is mismatch in either direction: an over-formal message to a colleague reads as cold, and a breezy one to a regulator reads as careless.
- Directness — how long the reader waits for the pointMeasurable as how far into the text the actual message appears. Model drafts characteristically bury it: context, then framing, then the request in paragraph three. Directness is also culturally loaded, so what reads as efficient in one setting reads as blunt in another, which is a judgement rather than a score.
- Urgency — and whether it is realManufactured urgency is the most damaging tone failure in marketing writing, because readers have been trained to discount it entirely. If the deadline is real, state it plainly and it works. If it is not, urgency language costs you credibility on everything else in the message.
The same words land differently depending on who reads them.
This is what separates tone from every other property you can check. Word count is a fact about the text. Reading grade is a fact about the text. Tone is not.
The same message shifts entirely with the relationship. “Can you get this to me by Thursday” is efficient from a peer, curt from a new manager, and presumptuous from a supplier. Nothing in the sentence changed. What changed is who sent it and what the reader already assumed about them.
And with the situation. A cheerful register is right for a product launch and actively harmful during an outage, where it reads as not understanding the seriousness. Most severe tone failures are not badly written sentences; they are competently written sentences delivered into the wrong moment.
Which is exactly where AI drafts fail. A model knows nothing about your relationship with the recipient, so it picks a default — usually a moderately formal, faintly upbeat register suitable for a stranger — and applies it to everything. Sent to a close colleague that reads as cold. Sent during a crisis it reads as oblivious. The writing is fine; the calibration is missing, and calibration is the part that requires knowing something.
So the output here is a reading, not a verdict. It tells you this text is low on warmth and high on formality. Whether that is right depends on facts about your reader that only you have.
Where tone goes wrong, and what actually works.
Four situations account for most tone disasters, and models handle all four badly in the same direction: they soften.
Delivering bad news. Say what happened in the first sentence. Do not open with regret, do not hedge the fact, do not bury it under context. Then be specific about what happens next. Softening is what makes bad-news messages feel evasive, and evasive is what people remember.
Saying no. A clear no with a brief reason is kinder than a warm maybe, because a maybe transfers the work of interpretation to the other person. Model drafts hedge refusals almost invariably, producing messages the recipient has to read twice to establish that they were declined.
Responding to a complaint. Acknowledge the specific thing that went wrong before anything else. Generic apology language — regret any inconvenience caused — signals that the complaint was not read, which is the original grievance repeated. Name what happened, then say what you are doing.
Apologising. The conditional is what ruins these. “I am sorry if this caused frustration” is not an apology; it is a hypothesis about the reader's emotional state. Say what you did, say it was wrong, say what changes. Models produce the conditional version constantly because it is the safest-sounding option, and it is the one that lands worst.
The default tone of ChatGPT, Claude, Gemini, DeepSeek, Grok, LLaMA and Perplexity.
Each has a resting register it returns to whatever you asked for, and knowing which tells you what to check.
ChatGPT tone analyzer
One tone, applied to everything, pitched slightly above the situation.
ChatGPT defaults to a helpful professional register — courteous, moderately formal, faintly upbeat — and holds it whatever you asked for. It reads as competent and it reads as the same voice every time, which is why organisations that draft everything with it end up sounding uniform across channels that should not sound alike.
Ask for a change of tone and it adjusts vocabulary while keeping the structure, so a “casual” version arrives with contractions and the same three-part shape underneath. Tone lives in structure as much as in word choice, and that is the part it does not move.
Claude tone analyzer
Reads as thoughtful, and overshoots into diffidence.
Claude qualifies heavily, which produces genuine care and, at volume, low confidence scores. A request wrapped in acknowledgements of the reader's time and caveats about whether now is convenient reads as apologising for having written at all.
It is also the most consistently warm of the assistants, which suits customer-facing writing and is occasionally too much for internal notes, where warmth in place of a decision reads as avoidance.
Gemini tone analyzer
Structure flattens tone, which is a real effect rather than an artefact.
Its bulleted output has almost no tone at all: lists are neutral by construction, and a message delivered as a list reads as filed rather than as sent. That is fine for a status update and wrong for anything where the relationship matters.
If you need a Gemini draft to land warmly, the first edit is converting it to prose. Tone lives in connective sentences, and bullets remove exactly those.
DeepSeek tone analyzer
Technical register regardless of the brief, which reads as cold.
Its output has the flat precision of documentation, and documentation has no addressee. Applied to a customer message that lands as indifferent, even when the content is entirely helpful, because nothing in it acknowledges that a person is reading.
Warming it requires structural change rather than word swaps: second person, an acknowledgement of what the reader is dealing with, and shorter sentences that leave room to breathe.
Grok tone analyzer
The most personality and the least calibration.
Grok is genuinely warmer and more direct than the others, which is an advantage in contexts that want a voice. What it does not do is modulate: the same register arrives for a product announcement and a service outage, and informality during a failure reads as not taking it seriously.
The check worth running on Grok drafts is situational rather than stylistic. The tone is usually good; the question is whether it is good for this particular message.
LLaMA tone analyzer
Tone varies within a document, which readers notice before they can name it.
LLaMA is weights rather than a product, so register depends on the deployment and the system prompt in front of it. A document assembled across sessions can shift tone between sections, and inconsistent tone is more damaging than any single wrong tone because it makes the writing feel assembled.
Check by section rather than as a whole. The average conceals exactly the problem.
Perplexity tone analyzer
Sourced writing has a distinctive and often unwanted register.
Because it writes to be cited, its tone is impersonal and hedged toward evidence. That is right for a briefing and wrong for correspondence: sentences deferring to authorities the reader cannot see come across as evasive rather than as rigorous.
Strip the citations and the deference remains in the sentence structure. Rewriting those sentences to state what you think, in your own name, is usually the whole fix.
Tone analyzer questions.
What does an AI tone analyzer check?
How writing lands on a reader across dimensions that move independently: warmth, confidence, formality, directness and urgency. It reads the signals that produce each — second person, hedging, sentence length, how far in the point appears — and says what the text is likely to feel like to somebody receiving it.
Is there a correct tone?
Only relative to a reader. Tone is not a property of text the way word count is; the same paragraph is warm from a colleague and cold from a stranger, appropriate in a memo and badly wrong in a condolence. That is why this page is organised around situations rather than around a scale, and why a tone reading is only useful once you have said who is reading.
Why does AI writing sound impersonal even when it is polite?
Because politeness and warmth are different things. A model produces courtesy reliably — please, thank you, apologies for any inconvenience — and warmth requires knowing something about the reader. Formulaic courtesy with no acknowledgement of the actual situation is precisely what reads as a form letter.
Can it tell me if my email sounds rude?
It can flag the signals that usually read as brusque: no opening, imperatives without softening, very short sentences, no acknowledgement of the recipient's position. Whether that is rude depends on your relationship with them, which is the judgement you have and the tool does not.
Can it check tone in ChatGPT, Claude and Gemini text?
Yes, and the sections above cover each. In short: ChatGPT applies one moderately formal register to everything, Claude is warm and diffident, Gemini's bullets remove tone entirely, DeepSeek reads as documentation, and Grok has personality without modulating it for the situation.
How do I make writing warmer?
Second person, contractions, and one sentence acknowledging what the reader is dealing with before you get to your part. Those three do most of the work. What does not work is adjectives — describing yourself as delighted does not make a message warm, it makes it about you.
How do I sound more confident without sounding arrogant?
Remove qualifiers that are not doing work, and keep the ones that are. Test each: if deleting it would make the sentence false, it is precision and it stays. If not, it was a reflex. Confidence comes from stating what you think and being specific about what you do not know, rather than from hedging everything equally.
Does tone affect how AI-generated my writing looks?
Indirectly and noticeably. Uniform tone across a whole document — the same register, the same warmth, paragraph after paragraph — is one of the things that reads as machine-made, because people modulate as they write. A tone that never varies is as much a tell as a sentence length that never varies.
What tone should a bad-news message have?
Direct and warm at once, which is the hardest combination and the one models handle worst. Say what happened in the first line, do not open with regret, do not hedge the fact itself, and be specific about what happens next. Softening the news is what makes bad-news messages feel evasive.
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.