Paste a real prompt. Pick your model. See what that one request costs, what a full conversation costs, and what a month of the same habit costs across Claude, Gemini, Copilot, ChatGPT and Grok.
Nothing you type leaves your browser. No prompt is sent anywhere. All math runs on this page.
The essay behind this page: Return on Token Investment, on what a month of Fable 5 actually taught me about the bill.
Type or paste what you would actually ask. The estimator counts tokens the way the models do, then prices the whole job, not just the sentence you typed.
Paste the real thing. Length is what costs money.
Published API rates per million tokens, checked 9 August 2026.
Cost per prompt is trivia. Cost per month is a decision.
Your exact prompt, your exact settings, run on a comparable model from each of the five. Choose the tier you would realistically use.
Most people never touch an API key. They pay a flat monthly fee. So the real question is whether your usage is worth more or less than the subscription.
Nobody wins this by picking the lowest price per million. They win it by needing fewer turns, sending less dead weight, and knowing which jobs deserve a frontier model and which deserve the cheap one. That is a skill, and it is learnable in an afternoon.
The simulator above is free and always will be. What is below it is the analysis: where the money really goes, and the changes that move it.
Every one of these is free to do today. The savings are typical ranges, not guarantees, and they compound.
The single biggest lever. One long thread re-sends its whole history on every turn. Three focused threads do not. When the subject changes, start over and paste in only what still matters.
Reformatting a list, extracting fields, writing a subject line: a light model does it identically for a twentieth of the price. Save the frontier model for judgement, ambiguity and code that has to be right.
Extended reasoning is billed at the output rate and can quietly triple a bill. Use it for analysis, planning and debugging. Do not use it to rewrite a paragraph.
Output costs four to six times what input costs. "In under 150 words" is a price control. Left unsaid, models default to long.
Put instructions, role and reference material at the top and never edit them. That prefix becomes cacheable and reads back at roughly a tenth of the price. Change one word at the top and the whole cache is void.
A 200 page PDF is about 150,000 tokens on every turn it stays in context. Paste the three relevant pages instead. Some models also charge a premium above a 200,000 token context.
Every connected integration ships its schema as input on every turn. Nine idle connectors is a standing charge for nothing.
Context, a specific task, the format you want, and one example. Four extra lines up front routinely removes three retries, and each retry was carrying the whole conversation with it.
Both major providers discount asynchronous batch work by 50%. If you do not need the answer in this minute, you are paying double for impatience.
If a conversation costs eleven cents and saves you forty minutes, stop optimising the eleven cents. Spend it faster and more often. The mistake is not overspending on tokens. It is underusing them on the work that would have paid.
You do not need to know how a transformer works. You need to know what you are buying.
What it usually means is that nobody ever designed the stack underneath it, so every job runs on whatever tool happened to be open. That is fixable in one sitting. Your scenario is where it starts: I audit what you actually run on, then build the stack around the work you actually do.
Your settings become the first page of the audit. Your prompt text is never part of it, only the choices you made.