Estimate your monthly LLM API bill from tokens per request and request volume, with editable per-model rates.
Token costs are asymmetric: output tokens usually cost about 5x input tokens, so a chatty prompt that produces short answers is cheap, while a short prompt demanding long essays is expensive. Measure a handful of real requests (most provider dashboards show token counts per call), take the average, and multiply by honest volume. Then add 20-30% headroom: retries, longer conversations and system prompts all inflate real usage past the back-of-napkin number.
The biggest levers, in order: caching repeated context (many providers discount cached input heavily), choosing a smaller model for classification-grade work, and trimming system prompts you paste into every call.
Provider pricing changes several times a year. The presets get you in the right ballpark; the rate fields are editable so you can plug in today's exact numbers from your provider's pricing page.
Everything you send (system prompt, history, the user message) is input; everything the model generates is output. Output is typically about 5x the price of input.
Cache or shorten repeated context, cap max output tokens, and route simple tasks (classification, extraction) to a cheaper model while keeping the expensive one for reasoning.
The n8n Launch Pack automates the operations behind these numbers, and The Prompt Operating System covers the decisions. Both are a one-time payment away (PayPal or USDT).
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