LLMPrice.io
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Audit your LLM API bill.

Drop last month's usage export and see what the same usage would cost on cheaper models, what prompt caching would have saved, and how your workload mix compares to the published indices.

What the audit checks

The cache gap first. Instructions and schemas that repeat on every call bill at a fraction of the standard rate when cached. The audit prices your input against each profile's assumed cache share, because that saving needs no model change at all.

A like-for-like switch. Your exact token counts priced on every current first-party model, ranked. Comparisons stay within tier: pointing you from a flagship at a bargain model is not a saving, it is a different product.

Your mix against the index. Every row is matched to the published workload profile with the nearest output share and compared to that profile's basket average, using published thresholds.

The audit is arithmetic on your token counts. It cannot see your latency budget, your evaluation results, or the one prompt that only works on the model you are already paying for. A written review by a person is free for the first five.

Related: How to audit your bill · Reading the OpenAI usage CSV · Verifying your Anthropic cache rate · Estimate a project instead →