RAG Chatbot API Cost Calculator
A RAG chatbot answer usually includes more input tokens than a normal chat reply because the model receives retrieved document chunks, instructions, chat history, and the user question.
Starting estimate
Input tokens
8,000
Output tokens
1,000
Preset name
rag-chatbot-answer
These values are only a starting point. You can adjust them on the calculator page.
What counts as input tokens?
System prompt, user question, chat history, retrieved document chunks, formatting instructions.
What counts as output tokens?
The generated answer, citations, sources, structured JSON, or follow-up suggestions if your app includes them.
What affects the cost?
Number and size of retrieved chunks, chat history length, model choice, context window requirements, and answer length.
FAQ
How many tokens does a RAG chatbot answer use?
It varies by chunk size and chat history, but 8,000 input and 1,000 output is a practical baseline.
Why are RAG input tokens often higher than output tokens?
Because retrieval chunks and prior messages are added to the prompt before generation.
Do I need a long-context model for RAG?
Use long context when chunked documents plus history exceed smaller context windows.
How can I reduce RAG chatbot API costs?
Trim retrieval chunks, shorten history, and cap output length where possible.
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