DeepSeek originality checker

A single call to a classification model, carrying your pasted draft, returning written findings. It reads for the editorial properties of originality: whether the framing is interchangeable, whether the structure arrived from habit, whether the assertions are attached to anything. That is a real question and this is a reasonable instrument for it.

It is not a similarity checker, and the difference is architectural rather than a matter of degree. A similarity checker needs a crawled, stored, searchable body of other people's text. This codebase contains no crawler, no store and no similarity search. The number of documents available for comparison is zero, so there is no smaller version of that product hiding in here, and no percentage that could be computed from it.

Short procedure, with the interpretation problem in the last step.

Step 1

Paste the draft, 1 to 12,000 characters after trimming. Section by section for anything longer, because the response length is capped.

Step 2

Constrain the read in the instruction field, up to 300 characters. Naming what to ignore is usually more effective than naming what to examine.

Step 3

Treat each finding as a claim about the text that you can check yourself by rereading the passage. The findings are arguments, not measurements.

Understanding the other product clarifies this one. A plagiarism checker crawls and indexes text, then compares your document against that index. GPTZero describes its checker as one that "scans your text and compares it against content that already exists, typically against a wide database of online sources including academic papers and articles", claiming coverage of over 100 million scholarly articles, research papers, websites and books. Its similarity score is a direct report of how much of your text the index matched.

Every property people value in that product follows from the corpus: the percentage, the source links, the highlighted passages. Remove the corpus and none of them can be reconstructed by any amount of model quality, because the model has nothing to compare against. That is why this page does not offer a reduced version of them. It offers a different reading entirely.

Both senses of the word are legitimate, both are measurable in some fashion, and each is blind to exactly what the other sees.

Not copied

Measured by matching against an index. Blind to sameness that is not copying, which is the dominant failure mode of generated text.

Not formulaic

Assessed by reading. Blind to copying, because a plagiarised passage can be beautifully written and this instrument would praise it.

Why generated text breaks the first

A DeepSeek draft is composed rather than lifted, so it typically matches nothing. A clean similarity report on such a text carries almost no information.

Why the second is harder to game

Rephrasing defeats a matcher. It does not defeat a reader noticing that the piece would survive having its subject swapped.

The practical rule

Ask which failure you are exposed to. Academic and legal exposure is the first. Editorial and reputational exposure is the second.

Every page ranking on this term leads with a figure. One fetched for this brief reports the share of sentences reading as human, so 85 percent human means roughly 85 percent of sentences carried human characteristics. That is at least a defined quantity, computed from a per-sentence classifier.

We run no such classifier and hold no index, so any number we printed would be a model's impression formatted to look like a measurement. Formatted precision is worse than no precision, because it invites decisions the underlying evidence cannot support. Written findings are less satisfying and more honest, and they can be checked by rereading the passage they point at, which a number cannot.

The system prompt this route runs under is short and worth publishing in full, because it is the actual constraint rather than a policy page: analyse only the supplied text for the qualities implied by the tool name, give a concise useful result with clear findings, never claim certainty about authorship, plagiarism, or which AI model wrote text, and describe observable signals and limitations instead.

Three refusals are encoded there. No authorship verdict, no plagiarism verdict, no model attribution. If a result you get here ever reads as certainty about any of the three, treat it as the model overreaching its instruction rather than as information.

The question behind most searches on this term is whether a document will clear something: a university's submission check, an employer's screen, a client's policy. We cannot answer it. Those systems are third-party products with their own corpora, thresholds and update schedules, none of which we can see.

We are not affiliated with Turnitin, Copyleaks, GPTZero or Originality.ai, we do not predict their output, and we will not describe any text as undetectable. Detection tools report signals rather than proof of authorship, which is also why a confident verdict from any of them, including a favourable one, deserves the same scepticism as a confident verdict here.

When you need evidence rather than an opinion. When the question is compliance with somebody's policy. When the text is confidential, because it goes to our server. And when a free deterministic check would answer the question faster: the browser-based checker on this site annotates 17 phrase patterns locally, costs nothing, and settles the vocabulary question before you spend a run on the argument.

It is the right instrument when a draft is defensible and dull, and you want a second reading that says so before a reader does.