Small models.
Specific jobs.

models

Model details

Each one does a single job. They share the same API, the same prepaid credit and the same per-token billing; rates are in what it costs.

pricing

What it costs

Credit is denominated in USD, not in an invented currency, so the balance on your dashboard is the amount of money you have left. Nothing expires on a monthly cycle and there is no seat count.

In practice: restyling a thousand-word article costs about $0.0002, and one fan-out call about $0.00001. You are billed per token on what you actually send, with no per-request fee.

What models are there?

Two, both live. Restyler 1 rewrites machine-written prose so it reads like a person wrote it. Query Fanout 1 takes a question someone would ask an AI assistant and returns the web searches that assistant is likely to run before answering. Same API, same key, same prepaid credit, same per-token rate.

What is Restyler actually for?

Rewriting machine-written prose so it reads like a person wrote it, without changing what it says. Generated drafts, documentation, product copy assembled by a pipeline. It preserves facts, names, numbers, links and formatting; it is not a summariser and not a general-purpose rewriter.

How good is Restyler, honestly?

On our held-out benchmark it keeps every fact in 41% of blocks and 73% of facts on average, with zero invented first-person voice and no repetition loops. That means roughly a quarter of blocks lose something, so it is not a substitute for review where accuracy is critical. The full numbers, including where it is weakest, are on the model page.

What is Query Fanout for?

Generative-engine optimisation. When someone asks an assistant about your category, the assistant runs its own searches before it answers, and the pages that rank for those searches are the pages it can cite. Query Fanout returns up to six of those searches for a question, which is a more useful brief than a keyword list. It is built from searches actually observed from ChatGPT, Claude and Gemini, not from queries invented by prompting a larger model.

Does it tell me what ChatGPT will really search?

No, and treat anything that claims to with suspicion. Two runs of the same question on the same engine share none of their query strings, so the target is not a fixed list. What the model gives you is the likely directions. Measured on coverage it scores 0.563 against a hand-written rule at 0.530, where one real engine run predicting another scores 0.75 to 0.91. That ceiling is the honest upper bound and we are well below it.

Is this an AI-detector bypass?

No, and we would rather you did not use it as one. It was trained for naturalness under strict semantic preservation; evasion was never an objective and is not measured. Detector scores move around with every detector release, so anyone selling you a guarantee there is selling you something they cannot deliver.

Why not just use a frontier model?

You can, and for a one-off it is fine. But asking a large model to do a narrow job costs orders of magnitude more per token, and on restyling it measures worse on preservation: frontier models tend to compress, dropping content while producing text that reads well. On fan-out, a frontier model prompted blind on the same labels scored 0.082 lower on coverage, though it returned two queries where ours returned six and coverage rewards more attempts, so some of that gap is budget rather than skill.

Is the API really OpenAI-compatible?

Yes for the wire format: point any OpenAI client at meragpt.com/v1, change the model name, and it works. Two honest caveats. Sampling parameters like temperature and top_p are accepted and ignored, because each model's decoding is fixed at whatever measured best for its job: greedy for Restyler, sampled for Query Fanout, where a single greedy answer collapses into six rewordings of one search. And streaming arrives a paragraph at a time rather than a token at a time.

What happens when my credit runs out?

Requests return a 402 with a message saying so, and nothing breaks silently. Auto top-up, if you enable it, charges your card when the balance falls below a level you pick. You can set a monthly ceiling, above which it stops and emails you instead of carrying on.

Do you train on what I send?

No. Requests are not used as training data. We record token counts, timing and cost for your usage dashboard and our billing; we do not retain the text of your requests or responses after serving them.

Does credit expire, and can I get it back?

Credit is valid for 12 months from the day you buy it, and after that we may expire whatever is left — we would email you first. It is not refundable, which is the normal arrangement for prepaid API credit. If something breaks on our side, write to us and we will sort it out.

What happened to the Hermes agent boxes?

That product ended on 2 September 2026 and existing subscriptions were closed out. meraGPT is now an API platform for small task-specific models. If you were a box customer and still need your data, email us.