What AI Detector Checker measures
All five guides describe the same check. Paste at least 100 words of English and the frozen V3 engine returns three things, and only these three things.
- A 0-100 AI-writing signal score. A fixed mapping of the classifier’s internal value for the whole passage. It is an estimate of signal strength, not the chance that AI wrote the text.
- A score stability range. How far that number moves when the source families behind the mapping are resampled. A wide range means the exact number is soft; the band it falls in is not decided by the number at all.
- One of three bands. Likely human-written, Uncertain or Likely AI-written, decided by thresholds that depend on the length of your text and were frozen before the sealed test.
What it does not return: a model name, a per-sentence verdict, a percentage of the document that is AI, or anything about who typed it. The detector never attributes text to ChatGPT, Claude, Gemini, DeepSeek, Copilot or any other product. How the detector analyzes text explains the pipeline; the evidence page publishes what it measured, including the two release targets the sealed test did not meet.
Why outputs from different models overlap
These guides describe tendencies, not fingerprints, and the tendencies overlap heavily. The current assistants are trained on overlapping data and tuned towards the same qualities — clear, balanced, evenly paced prose — so a summary from one reads much like a summary from another; the same product produces different writing depending on the instruction it was given, so a single prompt change moves the output further than a change of model would; a person editing a draft removes exactly the patterns a detector relies on, while a person writing formal, templated or translated English produces them without any assistance at all; and every model changes with each release, so a pattern that held six months ago may not hold now. That is why no page here converts a score into a product name, and why the guides are worth reading as descriptions of writing to review rather than as identification keys.
AI model detector FAQ
Can AI Detector Checker identify which AI model wrote my text?
No. AI Detector Checker measures general AI-writing signals. It does not identify ChatGPT, Claude, Gemini, DeepSeek, Copilot, a particular model family, or a model version. Similar outputs can come from different systems, and editing can obscure or introduce the same patterns.
Why provide model guides if the detector cannot identify the source?
People often need help with a particular AI workflow. The guides organize relevant use cases, interpretation advice, and limitations in one place. They help review text associated with a known or suspected workflow; they do not turn general signals into model-specific proof.
Do the model pages use different detectors?
No. The model pages explain the same general AI Detector Checker analysis. The overall result comes from a single document-level classifier; no individual sentences are marked. None of the pages performs source-model attribution.
How much text should I check?
Use a complete passage whenever possible rather than a title, single sentence, or short phrase. A longer sample offers more structure, rhythm, and variation to review, but it still does not make the result proof of authorship.
Can human-written text be flagged as AI-like?
Yes. Formal, repetitive, highly polished, translated, templated, or low-specificity human writing can share patterns with AI-assisted text. AI-generated text may also appear less AI-like after editing. Treat every result as a preliminary review signal and examine the wider context.