How is the expected cost computed?

Each attempt costs one generation plus one verification. The expected number of attempts is a geometric series: the initial attempt always runs, and each retry runs only if all prior attempts failed (probability = failure_rate raised to the number of prior failures), capped at your max retries.

Why does verification add cost?

A verifier must read the original input and the model's output to judge it, so it processes roughly the same token volume again plus its own rubric. Even a cheap verdict re-ingests the content, which is why the verify step is rarely free.

What is the residual failure rate?

It is the probability that every attempt — the initial generation and all your retries — hallucinates. It equals the failure rate raised to the power of total attempts. Adding retries shrinks it geometrically, but it never reaches zero.

Is retrying always worth it?

It depends on the cost of a wrong answer. If a hallucination is cheap to tolerate, a retry loop may not pay off. If a wrong answer is expensive or unsafe, paying a small cost multiplier to drive the residual failure rate toward zero is usually justified.

Is my data sent anywhere?

No. The calculator runs entirely in your browser. Nothing you enter is uploaded, stored or logged.

Cost of Hallucination Retry Loop Calculator

Cost of hallucination retry loop calculator

A common reliability pattern is to verify the model’s output and retry when it hallucinates. It works — but it is not free. Every retry costs another generation, and the verification step itself re-reads the content. This tool models the expected token cost of that loop and shows how much reliability you actually buy.

How it works

Each attempt is a generation plus a verification check. The model fails with probability p (your hallucination rate), so the loop continues until a verified-good answer or until you hit the retry cap. The expected number of attempts is a geometric series:

E[attempts] = 1 + p + p² + ... + p^maxRetries

The initial attempt always runs; each retry runs only if all earlier attempts failed. Multiply the per-attempt cost (generation + verification) by the expected attempts to get the expected cost per accepted result. The residual failure rate — the chance every attempt still hallucinates — is p^(maxRetries + 1).

Tips and notes

The cost multiplier is usually modest when the failure rate is low: at a 10% hallucination rate, the expected attempt count is only about 1.1, so you pay mostly for verification, not for retries. The bigger lever is the verification overhead — a lightweight validator (regex, schema check, or a tiny model) is far cheaper than a full LLM verifier that re-reads everything. Set max retries where the residual failure rate crosses your tolerance: two or three retries usually drives a 10% base rate below 0.1%. And weigh the loop against the cost of a wrong answer — if hallucinations are cheap to tolerate, skip the loop; if they are expensive or unsafe, the multiplier is well spent.