# The Hidden Math: How Your Prompt Design Silently 6x’s Your LLM Bill

> Source: <https://blog.devgenius.io/the-hidden-math-how-your-prompt-design-silently-6xs-your-llm-bill-f72a40702f6c?source=rss----4e2c1156667e---4>
> Published: 2026-07-21 09:39:08+00:00

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# The Hidden Math: How Your Prompt Design Silently 6x’s Your LLM Bill

For a while I assumed the model choice was the big lever on our API bill. Switch to something cheaper, save money — simple. Then I actually sat down and ran the numbers on our prompts themselves, and the model was barely the story. The prompt was.

Here’s the math, worked out on a realistic example, because the gap surprised me enough that I wanted to see it in black and white.

The formula is dead simple. Cost per request is just `(input tokens × input price) + (output tokens × output price)`

. The part people forget: output tokens usually cost about 5x more per token than input tokens. And cached tokens cost roughly a tenth of the normal input price on a hit. Those two facts alone explain most of what follows.

I’m using Claude Sonnet 5 pricing for this — $3 per million input tokens, $15 per million output — but the method transfers to whatever model you’re actually running. Token counts below are ballparked at about 1.3 tokens per English word, which is close enough for illustration; for your real numbers, run your actual prompts through the model’s token counter instead of eyeballing it.

**Exhibit A: the prompt nobody’s touched since launch. This is the classic first-draft system prompt — the one that grew by accretion, a sentence patched in every time**…