Free AI Detector
Paste any text to instantly check if it was written by ChatGPT, Claude, Gemini or another AI. Sentence-level highlighting and a probability score.
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What the AI Detector Looks For
AI-generated content has consistent statistical fingerprints. Our detector uses OpenAI token log-probability analysis to measure how "predictable" each word is given the words before it. AI models tend to choose highly probable, safe word choices, producing low perplexity scores. Human writing is less predictable and more varied.
Every analysis returns: An AI probability score (0-100%), sentence-level highlighting showing which sentences are statistically most AI-like, and word and character counts. For content that comes back AI-flagged, pair this with our AI Text Humaniser.
Log-Probability: Measures token predictability across the whole text
Burstiness: Detects lack of variation between sentences, a key AI signature
Sentence Highlighting: Flags individual sentences with highest AI probability
All Major Models: Detects patterns from ChatGPT, Claude, Gemini, and others
What We Detect
- ChatGPT Text
- Claude Output
- Gemini Writing
- AI Essays
- AI Cover Letters
- AI Blog Posts
- AI Emails
- GPT-4 Content
- Humanised AI Text
- Academic AI Use
Why Our AI Detector Works
Real log-probability analysis, not keyword matching. Sentence-level results, not just a single score.
Individual sentences are highlighted based on their AI probability score, so you see exactly which parts of the text are most suspicious.
Every result includes a confidence level based on text length. Short texts get Low confidence; longer samples get High confidence ratings.
Detects writing patterns from ChatGPT (all versions), Claude, Gemini, Copilot, Llama, and other LLMs using statistical methods that are model-agnostic.
The Bible, US Constitution, Shakespeare, and other canonical texts are automatically excluded to prevent false-positive AI flags.
Who Uses the AI Detector
From classrooms to content teams, anyone who needs to know whether text is human-written.
Check student essays for AI-generated content with specific, citable evidence at the sentence level.
Verify that cover letters and writing samples reflect the candidate's own ability.
Audit freelance and team-produced content before publishing to ensure it meets human-written standards.
Check AI-assisted drafts to identify which sections need more human voice before submitting.
How Accurate Are AI Detectors - And Why False Positives Happen
No AI detector is 100% accurate. GPTZero claims approximately 98% accuracy on pure AI text but carries a roughly 10% false positive rate on human writing under some conditions. Turnitin's AI detection has similar limitations and has been documented producing false flags on legitimate student work. False positives arise for three identifiable reasons.
First, highly structured human writing mimics AI patterns. Academic and technical writing trained on formal conventions - Clear topic sentences, consistent transitions, passive voice in methodology sections - Scores higher on AI probability metrics than casual prose. Second, non-native English speakers write more formulaically, using simpler, more predictable sentence structures that resemble AI output statistically. Third, certain topics produce predictable phrasing regardless of authorship - A human writing about the water cycle will use the same phrases as AI because the content domain constrains vocabulary.
A detector result is a probability estimate, not a verdict. Many academic institutions and employers are now advised not to take disciplinary action solely on the basis of AI detector scores. Use results as one signal among several, not as proof of AI authorship.
Understanding the Score: What Detection Results Actually Mean
AI probability scores reflect statistical predictability, not a binary classification. A score above 80% indicates the text closely matches patterns associated with AI-generated writing. Scores between 40% and 80% suggest a mixed document - Possibly AI-drafted and human-edited, or heavily structured human writing. Scores below 40% are consistent with human authorship.
Where shown, perplexity measures how surprised the model is by each token - Lower perplexity means more predictable, AI-typical text. Burstiness measures sentence-length variation - A flat score means uniformly-structured sentences, an AI characteristic. Running the same text through multiple detectors often produces meaningfully different scores because each tool uses different training data, different baseline corpora, and different thresholds for classification. This inconsistency is not a flaw - It reflects the genuine difficulty of the problem.
If you wrote something yourself and it flags as AI: Try submitting earlier drafts, or add more personal anecdotes, specific examples, and concrete details that only a human in your position would know. These additions both reduce AI scores and improve the writing itself.