LLM Token Counter & API Cost Calculator
Estimate prompt & output token counts, context window usage, and monthly API spend (with prompt caching) across OpenAI GPT-4o, Claude Sonnet, Gemini 2.5, and Llama.
Why Unit Economics Make or Break Production AI Products
When an AI prototype scales from 100 internal test runs to 250,000 monthly customer sessions, unoptimized system prompts and single-model routing can wipe out SaaS gross margins overnight. At Eternitech, we run multi-model LLM gateways across our own production platforms like Tabitha, combining model tiering, semantic prompt caching, and strict max-output token budgets.
Use this calculator to model real-world monthly inference costs across frontier and fast tier models—and see immediately how much prompt prefix caching saves on high-volume agentic and RAG workloads.
How to Model Your LLM API Spend
Paste a representative payload including system instructions, tool definitions, and retrieved RAG chunks:
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Frequently Asked Questions
How does prompt caching reduce LLM API costs by 50% to 90%?
Frontier providers (Anthropic Claude, OpenAI GPT-4o, Google Gemini) cache static prompt prefixes—such as system instructions, tool schemas, and few-shot examples. Placing static content at the very top of your prompt and dynamic user input at the bottom lets subsequent requests read from KV cache at a steep discount.
Is my prompt text sent to any server when using this calculator?
No. Token estimation and pricing calculations run 100% client-side in your browser. Proprietary prompts and schemas never leave your device.
When should we route to a fast model vs. a frontier model?
In production architectures built by Eternitech, lightweight classifiers, intent routers, and extraction tasks run on fast models (GPT-4o mini, Claude Haiku, Gemini Flash) while complex multi-step synthesis and code/tool reasoning route to frontier models.
