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

Prompt design can silently multiply an LLM API bill by six times, according to a cost analysis using Claude Sonnet 5 pricing. The analysis shows that output tokens cost roughly 5x more per token than input tokens, and cached tokens cost a tenth of the normal input price, making prompt structure the dominant cost driver over model choice. The author demonstrates that a first-draft system prompt grown by accretion can inflate token usage and cost far more than switching to a cheaper model.

Member-only story 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 …