Free AI Content Detector

SynthQuery's AI Content Detector estimates whether text reads more like typical human writing or like output from large language models such as ChatGPT, GPT-5, or Claude—before you publish, submit, or approve a draft. You get a clear verdict, sentence-level signals, and a heatmap so review time goes to the lines that matter most.

The detector runs server-side analysis on your pasted or uploaded text. It combines machine-learned classifiers trained on labeled human and model-generated examples with statistical cues that often differ between human and machine prose—such as how uniform sentence openings feel, how surprising word choices are in context, and how tightly phrasing follows common templates. Researchers frequently discuss related ideas with terms like perplexity and burstiness; in practice, SynthQuery turns those signals into an overall label such as Human, AI, or Mixed, plus per-sentence scores you can inspect instead of trusting a single opaque percentage. Standard mode is built for fast screening when you want a reliable first pass on a full draft. DeepScan uses a heavier analysis path tuned for mixed human-and-AI writing, lightly edited model text, or stylistically unusual passages where faster models disagree. DeepScan can take longer and may be limited to paid plans because it consumes more compute. In both modes you see highlights that show which sentences contribute most to the assessment, which makes the tool useful for editing and for explaining decisions to collaborators. Language detection helps you interpret results when the interface supports your locale; English tends to be the strongest-supported case across the industry because benchmarks and training data skew English-heavy. SynthQuery is designed for workflow use: rerun after meaningful edits, compare sections if a document is long, and treat the output as structured guidance rather than courtroom-ready proof. Text you submit is processed to return your results and is not used to train third-party foundation models through this product flow.

Students and educators use AI detection to support integrity policies when drafts may include undisclosed machine assistance—but scores should always sit alongside process evidence (outlines, drafts, citations) and institutional rules, not replace them. Marketing and communications teams run detection before campaigns go live so brand-owned copy can be reviewed consistently, especially when freelancers or agencies hand off drafts that may blend human editing with generative tools. Editors and publishers use sentence-level highlights to prioritize line edits: a few templated sentences often move the overall score more than polishing safe paragraphs. Customer support and documentation leads check macros and help-center articles where uniform phrasing is common and can resemble model tone even when written by people. Program managers who evaluate vendor-written content use detection as one quality gate within a broader checklist. Researchers and analysts should treat detectors as exploratory signals: short snippets, bullet lists, quoted material, and domain jargon can skew scores without implying misconduct. For any high-stakes decision—admissions, hiring, academic credit, or legal claims—pair tooling with human judgment, documented procedures, and awareness that no public detector is perfect across every genre and language.

Start with the full passage you will actually submit or publish—detectors work best with multi-sentence context. If you only have a short snippet, widen the window when possible (surrounding paragraphs) so the model can measure rhythm and variation; ultra-short inputs are often unstable. Choose Standard first for uniform drafts or when you need a quick decision. If the writing mixes personal anecdotes with oddly templated transitions, or if Standard returns borderline Mixed results, switch to DeepScan and allow extra processing time. Read the overall label, then open sentence-level highlights: edit or rewrite the highest-impact lines first, because they usually move the score more than cosmetic tweaks elsewhere. When you revise, make substantive changes—reorder ideas, replace generic openers, add concrete detail—rather than swapping a handful of synonyms, which can still read as machine-polished. Rerun detection after meaningful edits; small random changes should not be used to game scores in academic or professional settings. For long documents, analyze section by section at natural breaks so each run stays within limits and the heatmap stays interpretable. If you collaborate, export or screenshot results as needed for your review process, and combine the detector with plagiarism checks and your institution's disclosure rules where generative AI is permitted. Finally, if the result conflicts with what you know about how the draft was produced, trust your documentation and escalate through the proper human channel rather than treating any score as final truth.

AI detectors can be wrong: edited model text, collaborative writing, templates, and non-native English can resemble patterns associated with machines. Do not use scores alone for punitive decisions; provide writers a chance to explain process and show drafts. Prefer longer excerpts, disclose AI use where required, and keep detection as one signal in a broader review. When benchmarks disagree with your intuition, prioritize human judgment and policy.

On our internal benchmark of 10,000 human and AI-labeled samples (including GPT-class outputs), Standard ensemble mode reaches 99.2% agreement with document-level labels on the held-out split. Other vendors report different numbers on different corpora—we publish our methodology so you can compare claims fairly. Short snippets, edited AI text, or unusual style can still confuse any detector; use the heatmap as guidance, not proof.