MCP ROI calculator
Compare the cost of running data-heavy work straight in the chat against running it through an execution layer over a single MCP connection. See your yearly saving and return on every dollar spent.
Estimate only, at $3.00 per million input tokens and $0.008 per execution call. The 96.5 percent figure is a measured result on a data-heavy log task, not a guarantee for every query. The cost through the layer already includes the per-call fee.
How the ROI is worked out
Running heavy work in the chat means every raw token passes through the model and you pay for all of it. Running it through an execution layer means the work happens behind one connection and only the small result returns, so you pay for far fewer model tokens, plus a small per-call fee for the execution.
The calculator prices the in-chat path at three dollars per million input tokens, then prices the execution path as the reduced token cost plus a fee of eight tenths of a cent per call. The net saving is the difference, and the return is how many dollars you save for every dollar you spend running through the layer.
When the return is highest
The return climbs with how data heavy your work is. The proven 96.5 percent figure comes from analysing raw logs. You will see the strongest return when your queries involve:
- Reading and analysing large files, logs, or exports
- Searching the web and processing the results
- Querying databases and returning only what matters
- Running code or calculations over large inputs
Light, chat-style work saves less, which is why the workload selector lets you model a realistic mix.
Frequently asked questions
What does ROI mean here?
It is how many dollars you save for every dollar you spend running work through the execution layer. A 10x return means each dollar of execution fees avoids ten dollars of token cost. It already accounts for the per-call fee.
Why is MCP cheaper than doing it in the API directly?
It is not the protocol that saves money, it is keeping raw data out of the model. The execution layer does the heavy lifting behind one MCP connection and returns a small answer, so the model reads far fewer tokens on data-heavy work.
Does this work with ChatGPT and Gemini?
Yes. UniversalBench connects through one standard MCP link, so it works with Claude, ChatGPT, Gemini, and any MCP-compatible AI.
See the return on your own numbers
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