Llama AI Detector

Need to check whether a text was written with Llama? This free Llama AI detector reads English writing for the patterns that Llama and other AI models produce. Paste 100 words or more into the checker above, and V4, our current engine, returns an AI-writing signal score from 1 to 99, a stability range and a verdict, Likely human-written, Uncertain or Likely AI-written, together with the error rate measured on human writing of the same length. There is no account to create and your text is not kept.

Llama is the open-weights family among the models we cover, and it is the one where training made the biggest difference. V4 was trained on Llama writing, and in our sealed test it caught 963 of 990 Llama texts of 150 words or more. This guide explains how to check Llama text, how to read the result, and what our tests show about training on Llama.

Can a Llama AI detector tell if Llama wrote a text?

It can tell you whether a passage reads like AI writing. It cannot tell you that Llama, rather than another model, wrote it. V4 learned from nine families together, and a score describes writing patterns shared by many assistants, not a fingerprint of one model.

• It can score English text of 100 words or more and place it in one of three verdicts.
• It can catch most unedited Llama output: 97.27% at 150 words or more in the sealed test.
• It cannot name Llama or any other model, or tell a fine-tuned Llama from the base model.
• It cannot prove authorship. Editing, rewriting and translation all move the score.

What the Llama AI checker measures

V4 is a single fine-tuned transformer text classifier. It reads a passage in overlapping windows, averages them into one internal value and maps the result onto the 1–99 scale. It was trained on texts from nine model families, Llama among them, and on a large body of human writing dated 2021 or earlier, then frozen. It is never tuned on the texts people check.

The checker reads wording and structure only. It does not search the web, compare texts with a database or mark individual sentences; the score is for the passage as a whole. How the detector works explains the engine.

The score is not the share of the text written by AI, and it is not a probability. A result alone does not prove who wrote a text.

Why open-weights models are different

Llama’s weights are published, so anyone can run it on their own hardware, build it into a product or fine-tune it on their own writing. That makes Llama text harder to pin down than text from a single company’s app. The same base model may sit behind a customer-service bot, a writing tool or a privately trained assistant, each with its own style.

Our test used the base instruction-tuned model, not fine-tuned variants. A heavily fine-tuned Llama can write very differently, and nothing here measures it. For the same reason we also tested two open families that were never in training, gpt-oss and Gemma, to see how the engine copes with models it has never seen; their results are on the model guides hub and the evidence page.

How to check Llama text

1. Start from the text as received, before any edits.
2. Paste 150 words or more if you can. The checker accepts 100, but short Llama texts are the hardest case of any family (see below).
3. Run the check and read the verdict, score and stability range together.
4. Read the evidence line for how often human texts of that length reached the same zone.
5. Add other evidence: drafts, notes, sources and the writer’s usual style.

Use compare mode to put up to 3 passages side by side, and save the result as a PDF if you need a record.

Reading a Llama detector result

65 is the AI line at every length. Each length band has its own frozen internal threshold, and the score is mapped so that 65 always sits on it. The stability range shows how far the score moves when the source families behind the score map are resampled; the verdict is set by the frozen line. See understanding results for the full card.

With Llama, length matters more than for any other family. A human-zone result on a passage under 150 words tells you little; a longer sample gives a much clearer reading.

How V4 was trained on Llama text, and how well it detects it

The development tests showed that V4 depends on having seen Llama. When Llama was withheld from training, only 65.49% of its texts were caught, far below any other family. Llama was therefore kept in training, and the frozen engine was then measured once, on 17 October 2026, on new Llama texts that were locked away before training and never used to tune it.

The Llama part of the sealed set was written between 10 and 13 October 2026 over 35 genres, including newsletters, essays, product copy, interview answers and summaries, in 6 styles; half of the prompts started from human-written seed material. The texts came through the Together API, using the newest serverless instruction-tuned model available at generation time, at the provider default. Larger current Llama models were available only on dedicated hardware, so the family is represented by that one model, from a first build and a later batch. Only final answers were kept, and near-duplicates were removed.

What the results show:

• Training on Llama works. With Llama in training, 963 of 990 texts of 150 words or more were caught (97.27%), against 65.49% when it was left out in development.
• Short texts are the weak point. By length, 89.33% were caught at 100 to 149 words, the lowest short-text figure of any family, then 94.06% at 150 to 299, 98.44% at 300 to 599 and 98.75% at 600 or more.
• The previous engine caught 15.45% of the same texts.
• Rewriting tricks: no per-family attack figures exist; on the whole development set, the hardest of ten attacks, synonym swapping, left 97.40% of AI texts caught.

The evidence page carries every family and human source, and the methodology explains how the sets were built.

One real example

From the development set (never the sealed test): a how-to guide of 228 words written by Llama, which scored 93 (stability range 90–96), zone Likely AI-written. The opening, 80 words:

So you’re probably wondering what all the fuss is about with cloud storage, right, I mean I was too, it sounds like some fancy tech term that’s just a bunch of hot air, but apparently it’s actually a thing, it means you can store your files, like photos and documents and stuff, on the internet, instead of on your phone or computer, which is kinda cool, I guess, because it means you can access them from anywhere, as long as

Missed Llama text, edits and false positives

Llama has the most misses of any family: 27 of 990 texts of 150 words or more, and 16 of 150 at 100 to 149 words, stayed under the line. Editing adds to that. Text that has been condensed, reworded or translated was not tested and usually scores lower, so a low score is weak evidence that a passage is human.

False positives matter just as much. In the sealed test, human writing from sources the engine never trained on reached 65 about 1 in 700 times at 150 words or more (4 of 2,863) and about 1 in 100 at 100 to 149 words (11 of 1,136). Learners of English were flagged about 3 times as often as native writers at the same lengths, and formal speeches in 17 of 931 cases. If your writing was flagged, see what to do if your text is flagged.

Using a Llama checker responsibly

Treat the result as one signal among several. Look at how the text was produced, ask the writer, and check drafts and sources before you draw any conclusion. Never grade, reject or accuse anyone on a score alone, and be most careful with short passages, where Llama text and human text are both hardest to judge.

Llama AI detector FAQ

Other AI model guides

The same checker covers every family we tested. See all model guides, or compare with MiniMax, DeepSeek, the ChatGPT detector and the Claude AI detector.

Tested and written by the AI Detector Checker team. Last tested: 17 October 2026.