Llama Turnitin checker

One pasted section, read for the properties that make prose look machine-assembled: sentence length clustering, repeated paragraph openings, interchangeable transitions, hedging without commitment, and assertions that carry no specific instance underneath them. The report is written prose, capped at 1,400 tokens and produced at a low temperature.

It is worth being exact about the relationship this page has to the product it is named for, because the whole category is vague about it. There is none. No affiliation, no licence, no API access, no visibility into that system's model or its thresholds. What is offered here is a reading of your text, and nothing downstream of that reading belongs to anybody else.

A similarity report and an AI writing indicator solve different problems. Similarity matches your text against a corpus. An AI indicator has no corpus to match against, so it measures statistical regularity instead: as one explainer summarises it, machine-written prose tends to be statistically predictable, and sentence lengths stay consistent.

The presentation layer matters as much as the model. The same explainer reports that results below 20 percent are typically not shown to instructors at all, to reduce misinterpretation, that 20 to 50 percent reads as mixed, and that above 50 percent tends to trigger closer review. A visible number is therefore already the product of a configured reporting threshold, not a raw measurement, and access to it depends on institutional settings rather than on the student.

Served remotely. The section is posted to our AI server, read there, and the report returned in the same response.

Step 1

Paste one section of 1 to 12,000 characters. The bound is enforced before the model is called.

Step 2

Declare the section's function in up to 300 characters, since a methods chapter and a discussion chapter should be read against different expectations.

Step 3

Verify each finding against the passage. A finding you cannot locate in your own text is one to discard.

A university's guidance to its own faculty reports the vendor's figures: a 1 percent false positive rate on documents containing 20 percent or more machine-generated writing, and that the tool may miss about 15 percent of machine-written text in a document. Those two numbers are usually quoted separately and are far more informative together.

The costs are asymmetric. A false negative means a piece of work passes that should not have, which is an integrity problem for the institution. A false positive means a student is accused over writing they produced themselves, which is an academic and personal problem for one identifiable person. One percent of a large cohort is not a rounding error, and that asymmetry is the argument for treating any indicator as evidence to discuss rather than a finding to act on.

The explainer names the causes plainly: formal tone, technical terminology and heavy editing, with the observation that strong writing can sometimes look machine-generated. None of those is a fault, which is what makes this category difficult. The features that get a paper flagged overlap substantially with the features that get a paper a good mark.

One further case deserves naming rather than eliding. The vendor states its tool shows no bias against multilingual learners, and the same university guidance immediately notes that other evaluations have identified issues and that more peer-reviewed study is needed. The correct position is that this is unsettled, not that it is resolved in either direction.

Uniform sentence length

The single most cited statistical marker, and a common side effect of careful revision.

Formal register

Academic convention pushes toward exactly the impersonality detectors weight.

Technical vocabulary

Field-standard terms recur because they are correct, not because they were generated.

Heavy editing

Repeated polishing removes the irregularity that reads as human.

A percentage here would require either access to the third-party model or a calibrated classifier of our own. Neither exists. The number would be a language model's impression of a score, presented with the visual authority of a measurement, and a student who relaxed on the strength of it would have been misled by us specifically.

The system prompt behind this route forbids claiming certainty about authorship in any case. So the output stays at the level where it can be checked: named features, locatable in your own paragraphs, arguable by you. A percentage is unfalsifiable from where you sit, which is exactly why it feels reassuring and exactly why it should not.

Five runs per rolling 24 hours against a hashed identifier. Twelve thousand characters per run, so a full thesis is out of scope and must be sampled. No file upload, no batch, no export, no API. No similarity search, no corpus, no rewriting, and no output that highlights individual sentences as machine-written.

The text is transmitted to our AI server, unlike the watermark and hidden character routes on this site. It is not submitted to any academic repository, not retained as a paper and not indexed for future matching. And no prediction of any third-party detector's result is offered anywhere on this page, because none is available to us.

Candidates editing a final draft who want their own prose described rather than scored. Supervisors and markers who want the vocabulary to discuss a flagged submission without pretending the indicator settled it. Writers rebuilding a section that started as model output and who want the residual shape named.

The institutional posture is instructive here. One university disabled its AI detector in September 2023 and turned it back on in January 2024, and instructs faculty to treat the result as a starting point for a conversation rather than a strict enforcement tool. A tool that produced its own confident number would be pretending to a certainty the institutions running the real detector explicitly decline to claim.