How this calculator works
The Prompt Compression Savings Calculator estimates how much you save on API input costs when you shrink a repeated prompt. You enter your current prompt tokens per call, the compressed prompt tokens per call after trimming your system prompt and few-shot examples, your number of calls per month, and your input price per million tokens. It multiplies the token reduction per call by your monthly call volume and your input rate to show the monthly cost you avoid, then annualizes it. Because a system prompt and its examples are sent on every single request, even a modest per-call reduction compounds into a meaningful figure at scale, which is what this tool makes visible.
The key trade-off to watch is that fewer tokens can mean less context for the model. Trimming instructions or dropping few-shot examples may lower accuracy, increase retries, or produce longer outputs — and output tokens are usually priced higher and are not counted here, since this calculator only models input savings. Use it to prioritize compression on your highest-volume endpoints, where the same edit repays the most, but validate quality after each cut before assuming the saving is real. A prompt that is cheaper per call but needs a second attempt can cost more overall, so measure the response quality alongside the token count rather than optimizing tokens in isolation.
Frequently asked questions
How much does trimming my prompt actually save?
Every token you remove from a prompt is removed from every single call that uses it. Multiply the tokens saved per call by your monthly call volume and the input price per million tokens. A 1,000-token cut on a prompt run a million times a month at $3 per million saves $3,000 monthly — the size of the prompt matters far more than most teams assume because it recurs on every request.
Does prompt compression affect output cost or quality?
Compression only touches the input side — output tokens and their price are unchanged. Trimming redundant instructions, verbose few-shot examples and duplicated context usually keeps quality intact, but cutting too aggressively can hurt accuracy. Combine trimming with prompt caching for stable prefixes so the remaining fixed context is billed at the discounted cache-read rate.