Llama readability checker

GPTCleanup Pro's Llama readability checker reads a pasted passage of Llama output and returns an audit of how it actually moves: sentence length, paragraph density, an approximate grade level, and exactly which sentences are pulling that estimate up or down. It exists because of one fact that matters more for Llama than for almost any other model on this site: Llama is open-weight, so the same base weights get fine-tuned into dozens of separate products, each carrying its own house style. There is no single Llama voice to check a passage against.

It runs as a server tool. The passage is sent to our server, read there against sentence-length and vocabulary metrics, and the audit returns to the page. Five free checks in any rolling 24 hours, no account required, nothing kept once the result is returned.

This is a readability audit, not a grammar pass. It scores pacing and structure, not correctness. Run the companion Llama grammar checker when the question is whether the sentences are right, not how they read.

Ask what reading level a closed model's default output sits at and there is a real, if approximate, answer. Ask the same question about Llama and the honest answer is that it depends entirely on which product generated the passage, because the base weights are a starting point that gets reshaped downstream.

Fine-tune depth

A lightly fine-tuned deployment reads closer to the base model. A heavily fine-tuned one can read nothing like it.

System prompt

The instructions wrapped around the model by whichever product is running it push reading level up or down independent of the weights themselves.

Deployment age

Products rebuild their fine-tunes over time, so the same product can read differently across releases.

Instruction-tuning target

A deployment tuned for terse technical answers reads denser than one tuned for a general consumer chat interface.

One round trip, then a sentence-level breakdown to read against, not a single number to accept.

Step 1

Paste the Llama passage you want audited, up to 12,000 characters. A full passage produces a steadier estimate than one sentence.

Step 2

Run the audit. The passage is sent once to our server, scored for sentence length, structure and vocabulary, and the breakdown returns to the page.

Step 3

Read the flagged sentences alongside the overall grade, then judge this specific piece of output on its own terms rather than against an assumed Llama baseline.

For a model with a stable house style, a readability check mostly confirms or challenges an expected pattern. For Llama, the audit is frequently the first concrete evidence of what a specific deployment is producing, since there is no default to compare it against.

That distinction matters operationally. Two releases of the same product, built six months apart on different fine-tunes, can produce answers to the same question at noticeably different reading levels, and nothing about the product's own release notes would necessarily flag that. An audit run against both releases catches the drift directly.

A readability formula weighs sentence length and word difficulty, and both are shaped by decisions made downstream of the base weights: how deeply a deployment was fine-tuned, what data that fine-tune used, and what system prompt the surrounding product wraps around every response.

Because those decisions vary widely across the products that deploy Llama, the same base model can produce a plain, conversational answer in one context and a dense, technical one in another to an identical question. This audit reports what the specific passage in front of it measures, not an assumed baseline that would not hold across deployments anyway.

No, and a Pro tool should say so rather than imply otherwise. A single audit reports on the specific passage submitted, produced by whichever fine-tune generated it. It is not a claim about Llama as a fixed thing, because no such fixed thing exists across deployments the way it might for a single-vendor closed model.

It also cannot identify which fine-tune or product produced a given passage; the audit describes the text, not its origin. Treat each result as a spot check on one piece of output. Comparing samples from several deployments, checked separately, is the only reliable way to see an actual difference between them.

The same audit, run for different reasons depending on the role.

Platform teams

Comparing the reading level a candidate Llama deployment actually produces against the product's target audience before committing to it.

Developers

Spot-checking a self-hosted or custom fine-tune before shipping its output to users.

Editors overseeing several products

Confirming that Llama-drafted copy from different internal tools reads consistently for the same audience.

QA engineers

Running readability audits release over release to catch reading-level drift before it reaches users.