A wrong answer costs more than $0.000145 total

A model that looks cheapest by price per call can cost more in practice if its accuracy is lower and a mistake comes with any real cost. On the run of 4 November 2026, GPT-4o mini was the cheapest on every call, but not on every result: it missed 8% of answers, and the break-even for when a safer model is actually the cheaper pick is lower than most users guess [1].

The break-even: how a wrong answer flips the pick

On the 4 November 2026 run, GPT-4o mini cost $0.0000087 per answer and was wrong on 6 of 75 answers—8%, missing 2 out of 25 tasks every time they came up [1]. GPT-6 Luna cost more per answer ($0.0000203), but was wrong on none of the same tasks, in the same conditions [1].

The true cost per task is not just the price per call, but the price plus the expected cost from misses: `total cost per task = price per answer + miss rate × cost of one wrong answer` [1].

Worked out for these numbers:

• GPT-4o mini: $0.0000087 + 0.08 × (cost of a miss)
• GPT-6 Luna: $0.0000203 + 0 × (cost of a miss)

Solving for when the two are equal, the break-even is $0.000145 per wrong answer. If a mistake costs you more than that (just over one-hundredth of a cent), GPT-6 Luna becomes the cheaper model to run overall—even though its per-call price is higher [1].

What makes a wrong answer costly in practice

Most real-world tasks carry a hidden cost for mistakes. If a wrong answer triggers a user's time, a helpdesk ticket, a manual review, or a retry, the real cost is rarely as low as $0.000145 [1]. For example, if a user spends even one second rechecking an answer, or if a support agent needs to intervene, the cost immediately jumps far above this threshold.

The source notes that a wrong answer that leads to any human involvement or additional processing usually costs far more than $0.000145 [1]. In these cases, the higher-accuracy model is the more economical choice by a wide margin.

How the pick depends on your actual task

The cost flip seen in this run only holds for short, closed-ended, English-language tasks similar to those tested on 4 November 2026 [1]. If your real use case differs—longer prompts, open-ended questions, another language, or a different type of error—the miss rate, and thus the break-even point, may change. Likewise, if the model prices change, or if the errors occur on questions you do not care about, the cheaper model might win again.

For instance, GPT-4o mini's failures in this test were all on date calculations and a letter exclusion instruction—if your workflow never asks for those, its miss rate could be lower for your real tasks [1].

A worked example

Suppose you are deploying a simple support bot that answers user questions, and each wrong answer results in a support ticket. If resolving a ticket costs $0.50 in staff time, and your volume is 10,000 questions per day, the cost difference is dramatic.

• With GPT-4o mini's 8% miss rate: 800 wrong answers per day × $0.50 = $400 in support costs (ignoring the small per-call model cost).
• With GPT-6 Luna: 0 wrong answers, $0 in support costs.

Even though GPT-4o mini is cheaper per call, the accumulated cost of mistakes can outstrip any savings in model price by orders of magnitude, if each mistake has a real-world consequence.

What would change the answer

The pick changes if:

• The price per call moves: a price cut to the accurate model or a price rise for the cheap one shifts the break-even.
• The miss rate changes: if a model update or a different task set reduces the errors, the cheaper model can win again.
• Your use case has much higher or lower tolerance for errors: if a wrong answer does not matter, the lowest price wins; if each error is costly, accuracy dominates [1].

The site reruns the same benchmark as models and prices change, so you can see when the answer flips.

Questions readers ask

How do I estimate the cost of a wrong answer for my use case?

List what happens when the model is wrong: user complaints, staff reviews, retries, or lost trust. Assign a dollar value—use the cost of a minute of staff time or the value of a lost customer as a baseline.

What if I use a model for tasks where mistakes are harmless?

If a wrong answer has zero or negligible cost, pick the lowest price-per-call model that passes your task's pass rule. The break-even cost does not apply if you do not care about errors.

Does this result hold for every kind of task?

No. The measured break-even and miss rates are from a specific set of 25 short, closed-ended, English-language tasks. For other languages, open-ended questions, or longer prompts, run your own test or check this site for reruns with different task sets [1].

How the numbers work

These figures were computed for this article, not estimated by its writer. Inputs come from the numbered sources or are assumptions, labelled as such; change an assumption and the results change with it.

Sources

1. Our own data, 4 Nov 2026: Model bench, 2026-11-04 (raw answers and scores)