LLM cost calculator
Work out how much you spend on tokens each year, and how much you save when your AI does the heavy work through a single execution layer instead of burning tokens in the chat. Enter your numbers and press calculate.
Estimate only, based on the numbers you entered and a rate of $3.00 per million input tokens. Actual savings depend on your real workload. The 96.5 percent figure is a measured result on a data-heavy log-analysis task, not a guarantee for every query.
How this calculator works
Large language models charge per token, so the more raw text your AI reads, the more you pay. A query that pulls a whole log file, spreadsheet, or web page into the conversation can burn thousands of input tokens before the model even starts reasoning. Multiply that by every query, every day, across a year, and the bill grows fast.
The calculator estimates that yearly token volume from your inputs, prices it at three dollars per million input tokens, then shows how much you avoid when the heavy work runs through an execution layer first. Your AI sends one instruction, the work happens on the other side, and only the small final answer returns. The model reads a short result instead of the full dataset, so on data-heavy work the token count drops sharply.
When the saving is largest
The reduction depends entirely on how data-heavy your work is. The proven 96.5 percent figure comes from analysing raw logs, the kind of task where the AI would otherwise read a huge file token by token. You will see the biggest saving 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
Lighter, chat-style work where the AI mostly reasons over a short prompt saves far less, which is why the workload selector lets you model a realistic mix.
Frequently asked questions
How does an LLM cost calculator work?
It estimates what you pay for the tokens your AI processes. You pay per token, so cost equals total tokens times the price per token. This calculator takes your queries per day and the tokens each query would use, works out the yearly token volume, then shows how much of that volume and cost you avoid when the heavy work happens before the data ever reaches your model. It uses a rate of three dollars per million input tokens.
What is the 96.5 percent number based on?
It is a measured result on a data-heavy task, analysing a large log file. The AI was sent 4,024 tokens of raw logs and got the answer wrong, then the same task run through UniversalBench returned the correct answer using just 141 tokens, a 96.5 percent reduction. That is a real test, not a guarantee for every query. Lighter, chat-style work saves far less, which is why the calculator lets you pick a workload type.
How is the saving actually achieved?
Your AI offloads the heavy lifting. Instead of pulling a whole log file, dataset, or web page into the conversation and reasoning over every token, your AI sends one instruction through a single connection, the work runs on the other side, and only the small final answer comes back. The model reads a short result instead of a mountain of raw data, so the token count drops sharply on data-heavy work.
Does this work with ChatGPT and Gemini too?
Yes. UniversalBench connects through one standard MCP link, so it works with Claude, ChatGPT, Gemini, and any other MCP-compatible AI. You paste one URL into your AI and the capabilities appear.
What does UniversalBench cost?
The first 1,000 calls each month are free with no credit card. After that it is 0.008 dollars per call, with a 5 dollar minimum top-up. The calculator subtracts that cost so the net saving you see already accounts for what you pay.
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