Llama detector
What the Llama detector does
The Llama detector is a free Llama AI detector: paste in a paragraph, essay, email or comment, and it checks the writing against patterns typical of Meta's Llama models. It answers the question people type into a search bar, is this written by Llama, with a plain-language list of what it found and why, not a single score dressed up as a verdict.
This is a server tool, not a browser script. Your text is sent to our AI server to be scored; you get 5 free checks in any rolling 24 hours. There's no account to create, and this one names each pattern found so you can weigh the evidence yourself, which matters more here than with most models.
How it works
How to check text for Llama writing in three steps
It takes less time than reading the result does. There's no file upload, no formatting to strip out first, and nothing to configure before you run the check: paste, run, read.
- Step 1
- Paste the text you want checked. A paragraph is enough to run; a full page gives the check more to work with.
- Step 2
- Press Check text. It goes to our AI server once, gets scored against Llama's writing patterns, and the result comes back to the page.
- Step 3
- Read each flagged signal and the reason behind it, then judge for yourself. The detector explains its reasoning; it doesn't hand down a ruling.
Why Llama is different
Why Llama's writing habits vary more than other models'
Llama is open-weight, so dozens of companies and hobbyists fine-tune and deploy it with their own system prompts, for their own purposes. There's no single, stable Llama voice the way there is for a model served through one company's official product. A base model answering directly reads differently from a fine-tune wrapped in a support persona, which reads differently again from one tuned for role-play. The patterns below describe base Llama's default tendencies before a heavy fine-tune reshapes them.
- A generic assistant register
- Helpful, even-toned prose without a strong personality, closer to a neutral baseline than a distinctive voice.
- Straightforward, literal answers
- A tendency to answer the question as asked, with less elaboration or reframing than other assistants default to.
- Common hedging phrases
- “It's worth noting” and similar softeners, shared broadly across instruction-tuned models rather than unique to Llama.
- Variable formatting habits
- Whether an answer arrives as prose, a list or headers depends on which fine-tune and system prompt produced it.
- Inconsistent closing style
- Some deployments close with an offer to help further; others stop cold. This varies by product, not by the base model alone.
Straight answer
Can you tell which AI model wrote something?
Not with certainty, and for Llama specifically, less than for most. Llama, ChatGPT and Claude train on overlapping data and keep converging on similar phrasing, so the tells that separate them are already a matter of degree rather than a fingerprint. Layer on Llama's open-weight fine-tuning, and the signal weakens further.
A system prompt changes almost everything, too: tell a Llama-based assistant to write in a specific persona, and most of its default profile disappears. That's why this tool reports patterns typical of base Llama's default voice, not a claim about which model, which fine-tune, or which person wrote a piece of text. Treat a match as a lead worth a closer look, never an answer on its own.
Why flags happen
Why false positives happen
The patterns a Llama checker looks for are also, in places, just plain, direct writing. A neutral, even tone and literal answers are how a lot of people write by default, especially in professional contexts, with no model involved. A rigid house style favouring plain, unembellished prose reads the way base Llama does, for reasons that have nothing to do with a model.
Second-language writers get flagged often, unfairly. Straightforward, hedge-light sentences are exactly what a lot of plain-English writing guides teach. A false positive here isn't a flaw in the writer; it's a flaw in treating plain prose as proof of anything by itself.
If you're flagged
What to do if you're accused of using Llama
A flag is a signal, not a verdict, and no one should treat it as one on its own, especially given how much a Llama-based product's output varies by deployment. If someone has raised it against your work, the strongest response is evidence of your process, not an argument about the detector.
- Keep your drafts
- Earlier versions, outlines and notes are the plainest evidence a piece grew, rather than appearing whole.
- Keep version history on
- Google Docs, Word and most editors log revisions automatically. Don't clear it while a question is open.
- Be ready to talk through the work
- Someone who wrote a piece can usually explain a specific choice in it. That's harder to fake than the prose itself.
- Ask what the flag actually found
- A named signal can be discussed on its merits. A bare score can't, and it can't for a model this variable in particular.
Who uses it
Who uses the Llama detector, and when
Same tool, several reasons to open it.
- Editors and publishers
- Screening submissions for signs of an unedited AI draft before it reaches a human editor's time.
- Teachers
- Getting a second opinion on writing that reads unusually polished, as one input among several, not a grading tool.
- Hiring teams
- Checking a writing sample or cover letter for a voice that doesn't match the interview.
- Writers checking their own work
- Running a draft through before submitting it, to see whether their own habits read as machine-made.
- Developers using Llama-based tools
- Checking output from an app built on Llama to see how closely it tracks the base model's default patterns.
Free checks
How many free Llama detector checks you get, and why
This is a server tool: your text travels to our AI server to be scored, then the result travels back. That round trip makes the check possible, and it's why there's a limit.
You get 5 free checks in any rolling 24 hours, tracked without an account, resetting on a rolling clock, not a fixed hour. Five is enough for most real uses: one submission, one email, one flagged paragraph at a time.