Llama detector
What the Llama detector does
This Llama AI detector does one job: paste in text and find out whether it carries the habits Meta's Llama models tend to have. It's aimed at the question people search for, is this written by Llama, answering with a breakdown of specific signals, each with the reasoning attached, instead of one number pretending to settle something unsettled, a caveat that matters more than usual for Llama.
It runs as a server tool: text you paste goes to our AI server to be scored against Llama's baseline patterns, and comes back with the flags explained. You get 5 free checks in any rolling 24 hours, no account, no plan, no upgrade screen behind the free runs.
The output is a list, deliberately. A Llama detector that hands you a bare percentage asks for blind trust; this one names each pattern it found so you can weigh the evidence yourself, the right way to treat any result here.
Workflow
How to run the Llama detector on your own text in three steps
Three steps, and the third matters most. There's nothing to configure and no file format to match: paste plain text, get a plain-language result.
- 1. Paste your text
- Drop in whatever you're checking: a draft, a submission, a comment. More text gives the check a clearer pattern to read.
- 2. Run the check
- The text goes to our AI server once, gets scored, and the flagged patterns come back to the page. Nothing is queued or run twice.
- 3. Read the flags, not just a score
- Each signal ships with a plain-language reason. Use that to decide what the result actually means for your piece.
Why Llama is different
Why Llama's writing habits vary more than other models'
Llama is open-weight, and that changes how consistent its writing looks. Dozens of companies fine-tune it and wrap it in their own system prompts and personas, so a base model answering directly reads nothing like a customer-support bot built on top of it, which reads nothing like a role-play app built on top of that. There's no single Llama voice the way there is for a model served through one company's own product.
- A generic assistant register
- Helpful, even-toned prose with less distinctive personality than some other assistants default to.
- Straightforward, literal answers
- A tendency to answer the question as asked, without much elaboration or reframing other models reach for by default.
- 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 comes 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 just stop, a product decision more than a model trait.
Straight answer
Can you actually tell which AI model wrote something?
Not for certain, and for Llama specifically, less than for most models. Llama, ChatGPT and Claude train on overlapping material and keep converging toward the same phrasing habits already, so what separates them is a matter of degree, not a clean fingerprint, before factoring in Llama's fine-tuning ecosystem.
A system prompt can erase most of the remaining signal in one instruction: wrap a Llama-based assistant in a specific persona, and its default profile mostly disappears. What this app reports is patterns typical of base Llama's default voice, not a claim about which model, fine-tune, or person wrote the text in front of you. Read a match as a lead worth following up, not a finished answer.
That's also why this checker isn't marketed as a universal AI detector: a tool built around Llama's baseline habits pointed at a different model, or a very different fine-tune, is guessing outside its lane, so the site runs a separate checker tuned to each family.
Why flags happen
Why false positives happen
The patterns this app looks for are also, in places, just plain, direct writing. A neutral tone and literal answers are how plenty of people write by default, especially in professional or technical settings, with no model involved. A 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 plain-English writing guides teach. A flag here says something about a style choice, not the person who made it.
If you're flagged
What to do if you're accused of using Llama
A flag is a signal, not a finished case, and treating it as one hurts the person on the other end, especially given how much Llama-based output varies by deployment. If your own writing gets questioned, evidence of process beats an argument about the tool.
- Keep your drafts
- Outlines, early versions and marked-up notes are the clearest sign a piece was built up, not produced whole.
- Keep version history on
- Google Docs, Word and most editors log revisions automatically. Don't clear it while open.
- Be ready to talk through your choices
- Someone who wrote a piece can explain why a specific line reads the way it does. That's hard to fake.
- Ask what the flag actually found
- A named pattern can be discussed. A bare score can't, especially for a model this variable.
In practice
Who uses the Llama detector, and when
The same checker, opened for a handful of different reasons.
- Editorial teams
- A first pass on submissions, to route unedited AI drafts before committing editorial time.
- Teachers
- One input among several when a submission reads unusually polished, never the sole basis for a decision.
- Hiring managers
- Checking whether a writing sample matches the voice demonstrated live in an interview.
- Writers
- Running a draft through before submitting it, to see whether their own habits read as machine-typical.
- Developers building on Llama
- Checking output from a Llama-based product against the base model's default patterns.
The limit
How many free Llama detector checks you get
This app runs the check server-side: scoring against Llama's writing patterns needs a model too large to ship into a page, so each run is a single round trip.
That trip is metered, not unlimited: 5 free checks in any rolling 24-hour window, tracked without an account. There's no paid tier behind the limit; it reopens as the window rolls forward.
Five checks covers ordinary use: one submission, one email, one paragraph under review. For a longer batch, spacing checks across the day works better than burning the allowance at once.