AI Token Cost Calculator

You came here to find out what your tokens cost, and that part is arithmetic. Multiply the tokens one task reads by your model's input price per million, do the same for the tokens it writes, multiply by how many tasks you run in a month. The calculator below does it for six current models at their published prices.

Run it and the number will probably come out smaller than you expected. Mine did.

Here is the part the bill leaves out. It counts tokens, so it never sees the retry that fired when the first attempt came back wrong, the four minutes somebody spent reading an output that turned out fine, or the twenty minutes spent fixing one that did not. I cut average token cost on a live workflow about 23 times over and still could not call it a win, because nobody had measured any of that.

So put your own numbers in and look at cost per accepted task, what you paid for each piece of work that was actually good enough to use.

AI token cost calculator

1

Your token bill

Pick a model, or type the two rates off your own provider bill. Token counts are per task, across every call the task makes.

Claude Opus 5 GPT-5.6 Sol Claude Sonnet 5 Gemini 3.7 Flash Gemini 3.5 Flash-Lite GPT-5.6 Luna Your own price

Claude Sonnet 5: $2.00 per million in, $ 10.00 per million out. Launch rate, now standard.

Tasks the workflow attempts per month 500 count each piece of work you try to produce, whether or not it gets accepted

Input tokens per task 12,000 everything the model reads across every call the task makes, prompts and context included

Output tokens per task 2,000 everything the model writes, including reasoning tokens if your provider bills them as output

Tokens per month

7,200,000 in, 1,200,000 out

Token cost per task

$0.0528

Monthly token spend

$26.40

2

What the bill leaves out

Retries add to the token bill, and somebody still gets paid for the time spent checking and fixing the work. If you have not measured those hours, try an estimate and watch which input changes the answer most.

Attempts per task 1.2 each extra attempt repeats the token cost in this model

First-pass acceptance rate 70% the share that meets your written standard without anyone fixing it

Share of tasks a person reviews 60% the rest ship without anyone reading them

Review minutes per reviewed task 4 min average time spent checking a reviewed task, before any correction

Correction minutes per task that needs fixing 15 min average fixing time for every task that did not pass the first time

Loaded cost per hour of the person reviewing $60 salary plus payroll cost and benefits, not the rate you bill

Tools and infrastructure per month $200 the software, storage, and monitoring this workflow needs to run

Share of tasks nobody ever accepts 3% attempted, paid for, then abandoned or done again by hand

3

Cost per accepted task

Cost per accepted task

$7.58

144x the token price

Put your numbers in above, then compare the provider bill for one task with the estimated cost of one task that meets your standard.

Model tokens $26.40 · 1%

Tools and infrastructure $200.00 · 5%

Review time $1200.00 · 33%

Correction time $2250.00 · 61%

On the provider bill Paid somewhere else

Total monthly cost of this workflow

$3676.40

Share that never appears on the bill

99%

How AI tokens are calculated

Models do not read words, they read tokens, and a token is a word, part of a word, a punctuation mark, or a patch of an image. English runs somewhere near four characters to a token, so a thousand-word brief is roughly 1,300 of them. If you are budgeting off it rather than guessing, run your own text through your provider's tokenizer, because every model counts a little differently and a rule of thumb is only a rule of thumb.

Billing splits that count in two, and this is the half that surprises people. Input tokens are everything the model reads, which means your prompt, the system instructions, the documents you attached, and the whole conversation so far, again, on every single turn. Output tokens are everything it writes back, and on a reasoning model that includes the thinking you never see.

Output usually costs four or five times input at the same tier. So a workflow that reads a lot and writes a little is cheaper than its token count makes it look, and a chatty agent that keeps re-reading a long thread is more expensive than yours will look on paper.

After that it is two multiplications and an addition. Input tokens over a million times the input rate, plus output tokens over a million times the output rate, times the tasks you run in a month. Caching, batch tiers, and context-length tiers all move that number, which is why the calculator lets you type the two rates you actually pay instead of trusting a list price.

Do AI tokens cost money?

On a paid API, yes, every token, input and output, on every call. On a monthly seat for a chat product you pay a flat fee and the tokens live inside it. Most providers run a free tier with rate limits too, which is enough to test something and not enough to run delivery on.

For a business the honest answer is that tokens are the cheap part. Set the calculator to a small model and a normal month and the token spend often lands in the tens of dollars, which is less than one lunch with a client.

I have written the per-task version of this from my own production numbers. A conversation with an agent costs about eight cents at the median, and that cost is a dial you set, not a market price. The bill your firm actually feels is the hours around it.

Cost per accepted task

An accepted task is one that cleared your written standard. First-pass acceptance means it cleared it without anybody fixing it. If the work passes after a correction it still counts as accepted, and the correction time stays in the cost. Work nobody accepts still costs money, so you include that spending in the total but do not count those tasks as finished work.

Cost per accepted task = (model + tools + infrastructure + retries + review + correction) / accepted tasks

I ran that math on a real decision. I moved a recurring, rule-bound email-to-task workflow onto a cheaper model, and average model cost per attempt went from $1.5474 to $0.0659, about 23 times cheaper, with zero failed attempts on either side. Eleven attempts on the expensive model, ten on the cheap one. The dashboard stayed green the whole way.

I still could not call it a win, and that is the honest part. A green check says the system reached its last step without reporting an error, it says nothing about the work a person still has to do afterward. Nobody had measured whether a reviewer accepted the cheaper output as readily, so what I had was a test result, not a saving. OpenAI's July 14, 2026 guidance says the same thing in its own words, that a cheaper model may need more attempts, more correction, or more human review, and recommends tracking cost per accepted outcome on the workflows that matter.

Once you have the number it belongs next to the others. The Four Numbers covers what changed in the business: cost to acquire, cost to deliver, retention, and price. Cost per accepted task is what feeds the second one. A token bill on its own moves none of them.

Model prices used here

Every rate in the table was read off the provider's own pricing page on August 21, 2026. These are the standard paid rates for text, no batch discount, no cached-input rate, no cache-write premium, no priority tier, because those move the bill in both directions and belong in your own numbers rather than in a preset. Prices move, so check the source before you plan a quarter on one, and if your contract is different, type your own rates in instead.

Model	Input / 1M	Output / 1M	Source and caveat
Claude Opus 5	$5.00	$25.00	Anthropic · Standard rate. Cached input costs 0.1x this rate, batch costs half.
gpt-5.6-sol	$4.00	$20.00	OpenAI · Short-context tier. Long context costs $8.00 input and $30.00 output per million.
Claude Sonnet 5	$2.00	$10.00	Anthropic · Launch rate, now standard.
gemini-3.7-flash	$0.75	$3.75	Google · Through 2026-12-31, then $1.50 input and $7.50 output per million. Output includes thinking tokens.
gemini-3.5-flash-lite	$0.30	$2.50	Google · Output includes thinking tokens.
gpt-5.6-luna	$0.20	$1.20	OpenAI · Short-context tier. Long context costs $0.40 input and $1.80 output per million.

What this calculator does not know

It does arithmetic on the numbers you give it. There is no benchmark inside it and it is not a forecast, so it is only as good as your inputs, and the ones it starts with are round placeholders I picked to look plausible, not figures out of any study.

The bigger limit is that it cannot tell you whether the cheaper model does work that is just as good. You may pay less for tokens and more for someone to check and fix the answer, so you have to measure the whole job before deciding which model is cheaper for your firm.

It also does not model caching, and across my own examples the effective rate swings from about three cents per million tokens to thirteen depending on how hard the system caches. It treats review as a flat average per task when in practice a handful of hard ones eat most of the time. And it says nothing about whether the freed hours turned into anything, which is what the ROI calculator is for.

If you do one thing with the result, take the two numbers in stage 3 to whoever owns the workflow and ask which of those four costs they can actually measure this month. Start with the review minutes if that is the largest cost in your result, then measure it in the actual work before treating the estimate as a saving.