Free AI Content Detector
Paste any text to see how strongly it shows the writing patterns of AI assistants, which sentences trigger them and why. It runs entirely in your browser with no AI model and no upload, and it tells you plainly what it can and can't prove.
At a glance
- The score measures writing patterns, not authorship. 0 means few AI-style patterns; 100 means many.
- It's a lead, never proof. Every AI detector produces false positives, and edited AI text often passes.
- Google doesn't penalise AI text for being AI. It penalises low-value content made to manipulate rankings.
- Use the highlights to edit: cut stock phrases, vary sentences, add real examples and numbers.
How this AI detector reads your text
It measures 12 writing signals and weighs them into one score from 0 to 100. There's no language model behind it: no ChatGPT, no API and no upload. The checks are plain statistics about your sentences and words, and the weights were set by testing them on a public research dataset of matched human and ChatGPT answers (more on that below). That's why it's free to run as often as you like and why the text stays on your device.
| Signal | Typical of human writing | Typical of default AI writing |
|---|---|---|
| Sentence length variation | Mixes short and long sentences | Sentences of very similar length |
| AI-favoured words | Rare | Delve, crucial, comprehensive, notably, seamless, robust |
| Stock phrases | Rare | "It's important to note", "a wide range of", "in conclusion" |
| Transition openers | Occasional | Additionally, Furthermore, Moreover, Overall at the start of many sentences |
| Contractions | Common in most writing | Often missing in formal AI answers |
| Personal and informal words | I, my, honestly, pretty, yeah | Rare in default AI style |
| Vocabulary variety | Varied | Narrower: the same words repeat |
| Average word length | Shifts with the writer | Shifts with the model; weighted by the data |
| Repeated sentence openers | Varies | Several sentences opening with the same word |
| Average sentence length | Any | Often 18 to 25 words |
| Numbers and specifics | Dates, prices, counts from experience | Fewer concrete figures |
| Casual sentence starts | Lower-case starts, dashes, bullets | Almost always tidy capitals |
Some of the word list comes straight from research. A study of more than 15 million biomedical abstracts found that words such as delves, underscores and showcasing jumped in frequency after ChatGPT arrived, and estimated that at least 13.5% of 2024 abstracts were processed with a language model (Kobak et al.). The detector counts those words along with stock phrases that AI assistants reach for again and again.
Reading your score
| Score | Band | What it usually means | What to do |
|---|---|---|---|
| 0 to 34 | Few AI patterns | Reads like varied, specific human writing, or AI text that has been edited heavily | Nothing, unless facts or tone need work |
| 35 to 79 | Some AI patterns | A mix: formal human writing, lightly edited AI drafts, or blended text | Look at the highlighted sentences and the signals table |
| 80 to 100 | Strong AI patterns | Matches the default style of AI assistants closely | Edit for readers: specifics, varied sentences, fewer stock phrases |
The score is not "the percentage written by AI". A text scoring 90 isn't 90% AI; it shows many patterns that, in our tests, mostly came from AI. Likewise, a score of 5 doesn't clear anyone: lightly edited AI text often lands low.
We tested it: results on 1,492 held-out texts
On text it had never seen, the detector got 87.5% right. We calibrated it on the HC3 dataset, where researchers collected human answers and ChatGPT answers to the same questions (Guo et al., 2023). We used finance, Reddit "explain like I'm five", open questions and computer-science topics, kept only answers of 80 words or more, fixed the dataset's spacing quirks so the tool couldn't learn formatting instead of writing, trained on 70% of the pairs and tested on the other 30%.
| Measure | Result |
|---|---|
| Texts tested (half human, half ChatGPT) | 1,492 |
| Correct at the 50-point line | 87.5% |
| Human texts wrongly in the 80-100 band | 3.1% |
| AI texts in the 80-100 band | 66.6% |
| AI texts that scored under 35 (missed) | 5.9% |
| Strongest topic: finance answers | 91.3% correct |
| Weakest topic: computer-science explanations | 82.5% correct; formal human writing looks more AI-like |
We also ran three Paul Graham essays written before ChatGPT existed: they scored 0, 0 and 1. A deliberately generic AI paragraph (sample A above) scores 100. Then the honest part: AI-assisted drafts that a person had heavily edited scored between 1 and 2. That's the real limit of every detector, and why the score should start a conversation, not end one.
What no AI detector can tell you
No tool can prove who wrote a text. OpenAI built its own AI text classifier, reported that it caught only 26% of AI-written text while flagging 9% of human text, and withdrew it in July 2023 "due to its low rate of accuracy" (OpenAI). Researchers at the University of Maryland showed that simple paraphrasing sharply lowers detection rates, even for watermark-based detectors (Sadasivan et al.).
The fairness problem is worse. A Stanford study ran seven popular detectors on essays written by non-native English speakers for the TOEFL test: on average they labelled 61% of those human essays as AI-generated, and all seven agreed on about one in five (Liang et al.). Plainer vocabulary and simpler sentences look "predictable" to a detector. Our tool shares that weakness, which is why it explains every signal instead of handing down a verdict.
- Don't use it to accuse anyone of cheating or of breaking a contract.
- Don't treat a low score as a guarantee that a supplier wrote something by hand.
- Do use it to find generic passages in your own drafts and to edit them into something specific.
Does AI content hurt SEO?
Not because it's AI. Google's guidance is to reward "high-quality content, however it is produced", and it says using automation, including AI, "to generate content with the primary purpose of manipulating ranking in search results" breaks its spam policies (Google Search Central). Its spam policies name scaled content abuse: many pages made mainly to rank, with little value for readers.
So for a marketer, a high score is a quality warning rather than a penalty warning. Generic text full of stock phrases tends to be thin text, and thin text struggles whoever wrote it. Google's own helpful content questions ask whether a page offers original information, a substantial description and first-hand expertise. Those are exactly the things that also lower the score.
Turning a high score into better writing
Use the highlighted sentences as an editing list. Each of these edits helps readers first; the lower score follows.
- Replace stock phrases with the point itself. "It's important to note that prices rose" becomes "Prices rose 12% in March".
- Add something only you know: a customer question, a number from your own data, a mistake you made, a photo of the real thing.
- Vary the rhythm. Follow a long sentence with a short one. Like this.
- Cut opening transitions. Most sentences that start with Additionally or Moreover read better without the word.
- Swap inflated words for plain ones: use instead of utilise, help instead of empower, important instead of pivotal.
- Answer the reader's question in the first two lines, then explain.
- Check the facts. AI drafts state guesses confidently; every number needs a source.
Before and after, from our own testing: the sentence "Additionally, a comprehensive SEO approach ensures that your content resonates with users" scores as pure AI pattern. "Our three best pages all answer questions customers emailed us" carries the same idea as a specific claim a reader can check.
Who uses an AI content checker, and for what
| Who | Sensible use | Avoid |
|---|---|---|
| Content marketers and editors | Spot generic passages in drafts before publishing | Rejecting a freelancer on the score alone |
| Business owners hiring writers | Ask better questions about process and sources | Treating a low score as proof of hand-written work |
| Writers | Check whether a draft sounds templated, then add specifics | Rewriting just to game the score |
| Teachers and managers | Start a conversation about drafts and sources | Disciplinary decisions based on a detector |
How it compares with other free AI detectors
Most popular AI detectors send your text to a server and run a language model on it; this one doesn't. Model-based detectors usually look at how predictable each word is to a model, which can catch more subtle AI text but costs money per check, needs your text on someone else's server, and still produces the false positives described above. Ours trades some sensitivity for privacy, transparency and a free, unlimited check: you can see every signal and every highlighted word, so you can judge the result yourself.
AI content detection on one map
Your next step
Paste your next draft before you publish it. Read the highlighted sentences, replace stock phrases with specifics from your own experience, vary the sentence lengths, and check it again. Aim for a page a reader would bookmark, not for a particular score.
Publish articles built on research, not filler
Owlthor researches what ranks for your keyword, then writes the article with images, mind maps and charts and sends it to your CMS.
Frequently asked questions
Is this AI content detector free?
Yes. It's free with no sign-up and no limit on checks, because it runs in your own browser. Your text never leaves your device, and we don't use any AI model or paid API to check it.
How accurate is the AI detector?
On held-out text from the public HC3 research dataset it classified 87.5% of answers correctly, and wrongly put 3.1% of human answers in the "strong AI patterns" band. That dataset's AI answers came from early ChatGPT, so treat newer or edited AI text as harder: it often scores low.
Can an AI detector prove that text was written by AI?
No. No detector can. OpenAI withdrew its own AI text classifier in 2023 because of its low accuracy, and research shows detectors can be fooled by paraphrasing. Use the score as a prompt to look closer, never as proof.
Why does my human-written text score high?
Formal, polished writing shares many patterns with AI text: even sentence lengths, no contractions, transition words such as Additionally and Moreover, and words such as comprehensive or crucial. Writers working in a second language are especially likely to be misjudged, as Stanford researchers found.
Does Google penalise AI-generated content?
Not for being AI-generated. Google says it rewards high-quality content however it's produced. What breaks its spam policies is using automation, including AI, mainly to manipulate rankings, such as mass-producing pages with little value.
How much text do I need?
At least 80 words, and ideally 150 or more. Short text gives the statistics too little to work with, so the tool warns you when a result is based on a short sample.
Is my text stored or used for training?
No. The detector runs in your browser and nothing is sent to us or anyone else. You can even disconnect from the internet after the page loads and it still works.
Which AI models can it detect?
It doesn't identify models. It looks for writing patterns common to AI assistants, such as ChatGPT, Gemini and Claude, when they write in their default style. Text that was prompted to sound casual, or edited by a person, usually shows fewer of those patterns.
Should I use this to check students' or employees' work?
Not to make decisions. The false positives that every detector produces fall hardest on non-native writers. If you have concerns, talk to the writer and look at drafts and sources instead.
How do I make AI-assisted writing better?
Edit for readers, not for detectors: add first-hand examples and real numbers, cut stock phrases and filler transitions, vary sentence length, and answer the reader's question in the first lines. Those edits make the content more useful, which is what search engines reward.
Sources
- New AI classifier for indicating AI-written text (with July 2023 update), OpenAI, 2023
- GPT detectors are biased against non-native English writers (Liang et al.), Patterns / arXiv, 2023
- Can AI-Generated Text be Reliably Detected? (Sadasivan et al.), TMLR / arXiv, 2023
- Delving into LLM-assisted writing in biomedical publications through excess vocabulary (Kobak et al.), Science Advances / arXiv, 2025
- How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection (Guo et al.), arXiv, 2023
- HC3: Human ChatGPT Comparison Corpus (dataset), Hugging Face, 2023
- Google Search's guidance about AI-generated content, Google Search Central, 2023
- Spam policies for Google web search, Google Search Central, 2026
- Creating helpful, reliable, people-first content, Google Search Central, 2026
Agents that do this for you
Open source on GitHub