Llama detector

GPTCleanup Pro's Llama detector reads a block of text and reports the patterns typical of Meta's Llama models, with each signal named and explained, rather than compressed into one score that hides its own reasoning. It's built for the question people type into search: is this written by Llama. The answer here is a breakdown, not a verdict, and for Llama that framing matters more than for most models.

It's a server tool by necessity: the pattern model is too large to run in a browser, so text is sent to our AI server to be scored, and the result returns to the page. You get 5 free checks in any rolling 24 hours, no account required. The output is deliberately a list rather than a score, naming each pattern found so the reasoning is available for inspection rather than taken on faith.

One round trip to the server, then a reasoned result. Nothing to configure beforehand, no particular format required: plain text in, a breakdown out.

Step 1

Paste the sample. A longer passage gives the pattern model more structure to weigh; a single sentence rarely produces a confident read.

Step 2

Run the check. The text is sent once to our AI server, scored against the patterns below, and nothing further is done with it.

Step 3

Review each flagged pattern and its reasoning. The output is an argument you can inspect, not a number you're asked to trust.

Llama's open weights are the defining fact here. Since dozens of companies fine-tune and deploy it under their own system prompts and personas, there's no single stable Llama voice comparable to a model served exclusively through one company's own product. A base model answering directly, a customer-support fine-tune and a role-play fine-tune can each read entirely differently, despite sharing a common ancestor.

A generic assistant register

Helpful, even-toned prose without a strong distinguishing personality, closer to a neutral baseline than a signature style.

Straightforward, literal answers

A tendency to answer the question as asked, with less elaboration or reframing than some other assistants default to.

Common hedging phrases

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 the fine-tune and system prompt in front of the base model.

Inconsistent closing style

Whether a response closes with an offer to help further is a downstream product decision, not a fixed model trait.

Not with confidence for any model, and for Llama specifically, less than for most. Llama, ChatGPT and Claude train on overlapping corpora and continue to converge stylistically before any fine-tuning is applied; Llama's open-weight ecosystem then compounds that uncertainty further.

A system prompt further erodes the signal: wrap a Llama-based assistant in a specific persona and most of its default stylistic profile disappears in one step. What this detector reports is a match to patterns typical of base Llama's default voice, not a determination of which model, fine-tune, or person produced the text. Treat a strong match as grounds for a closer look, not a finding.

Several categories of legitimate human writing share these patterns for reasons unrelated to AI, and a serious detector says so rather than burying it.

Plain-English writers

A neutral tone and literal, unembellished answers are a deliberate style choice taught in plain-English writing guides, independent of any model.

Second-language writers

Straightforward, hedge-light sentence construction is standard ESL instruction, the exact material that produces flags here.

Technical support and documentation writers

A neutral, even-toned register is a professional norm in this field for reasons that predate any assistant model.

Rigid house styles

A style guide favouring literal, unembellished prose reads like base Llama's, for institutional reasons.

Text from a Llama-based product, not the base model

A heavily fine-tuned deployment carries a different signature entirely, and may not match the baseline patterns checked here.

A flag from this tool, or from any detector, is a signal rather than a finding, and should be treated that way by anyone using it to make a decision about your work, with extra weight given to Llama's variance.

Preserve your drafts

Outlines, early versions and marked-up revisions are the clearest evidence a piece was composed, not generated.

Retain version history

Docs, Word and most editors log revisions automatically. Leave it intact while a question is open.

Be prepared to discuss specific choices

Explaining why a line or structure exists is harder to fabricate than the prose itself.

Ask for the named signal, not the label

A specific pattern can be examined on its merits. An unexplained score cannot, and is least defensible for a model this variable.

The same checker, applied at different points in a review.

Editorial and publishing teams

A first pass on submissions, to route unedited AI drafts before committing editorial time.

Instructors

One input among several when a submission reads unusually polished, never the sole basis for a decision.

Hiring reviewers

Checking whether a writing sample matches the voice demonstrated live in an interview.

Writers reviewing their own drafts

Running a piece through before submission, to see whether their own habits are reading as machine-typical.

Engineering and product teams building on Llama

Comparing a deployed fine-tune's output against base Llama's default patterns as part of evaluating drift.

This detector is server-side by design: scoring against Llama's writing patterns requires a model too large to ship into a page, so each check is one round trip, text out, result back.

That trip is metered rather than 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 exhausting the allowance at once.