LLM Cutover

Free cutover audit · no signup

Your model, re-priced on your own data.

Paste examples your current model already labelled. A cheaper model answers the same task, and you get a short report: where it agrees, the confidence threshold that keeps quality, and what that does to the monthly bill.

Run the free auditPricing

candidate · conf ≥ 0.984% of calls

agreement on those99.0%

hybrid bill$174.30/mo −83%

Illustrative numbers. Yours come from your samples.

Run the free audit

First time? Fill the form with a worked example, then press Run.

What does your model do?

CSV text,label (label yes/no) or JSONL {"text": "…", "label": "yes"}.

Tokens per call

Reasoning (thinking) tokens are billed at the output price by OpenAI, Anthropic and Google. The number above is our estimate for the effort level on a short classify or extract call, not a provider figure. For your real number, read it from your API usage: OpenAI output_tokens_details.reasoning_tokens, Gemini total_thought_tokens, Claude counts thinking inside output_tokens.

Don’t paste personal or confidential data. Samples go to the candidate model (a US service) for this run and are not stored.

Pricing

Free audit

$0

25 samples, one task, 5 runs a day. Agreement, routing table, cost projection, router snippet.

Full audit

$49

One payment, no subscription. 5 runs within 30 days, up to 500 yes/no or 200 extraction samples per run, the full list of samples where the cheaper model disagrees (JSON and CSV), plus the per-field breakdown and router snippet.

See a sample full report (example data).

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Questions

Which cheaper model does it test?

Today: Levanto Sage 2, a classify/extract model priced at $0.05 per million input tokens that returns calibrated probabilities. The audit code is model-swappable; more candidates are being added.

Do you store my samples?

No. Samples are sent to the candidate model to run the audit, then dropped. Nothing is written to a database, and the report lives only in your browser. Don’t paste personal or sensitive data: the candidate model is a US service.

Why compare against my current model and not ground truth?

Because that is the cutover question: will switching change the answers your product already relies on? If you have human-checked labels, paste those instead and the same numbers become accuracy.

Is 25 samples enough?

It’s enough to rule a switch in or out, not to sign it off. One disagreement in 25 moves agreement by 4 points. The full audit (up to 500 yes/no or 200 extraction samples per run) narrows that.

What if my current model is already cheap?

Then the report says so. If you already run a small model at about $0.10 per million input tokens, a switch rarely saves enough to matter, and the audit will mark it “not worth it”.