Turn a fixed AI budget into text requests, output words, pages, tokens, and image-generation capacity.
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Words per dollar
153,846
Pages per dollar: 307.7.
Output tokens
2,051,282 tokens
Output tokens are estimated from words per output token.
Per-unit cost
$0.0049 / request
$0.2405 per image under the image-token assumption.
When to use this
Use it before buying a fixed output batch
This reverse calculator is for content batches, copy tests, image campaigns, AI writing budgets, and quick price-performance comparisons where the question is what a fixed budget buys.
Default result
The server-rendered $10 GPT-5.4 mini example buys about 1,538,462 words, 3,076.9 pages, or 41.6 GPT-Image-2 images under the editable assumptions.
Worked example: $10 blog-draft budget
The default scenario uses a $10 budget, GPT-5.4 mini, 500 input tokens, 750 output words, and 0.75 words per output token. Output tokens per request are 750 / 0.75 = 1,000.
Text request cost is 500 / 1,000,000 x $0.75 plus 1,000 / 1,000,000 x $4.50 = $0.004875 per request.
$10 / $0.004875 buys about 2,051 text requests. At 750 words each, that is about 1,538,462 words or 3,077 pages at 500 words per page.
For GPT-Image-2 planning, 100 text prompt tokens at $5 per 1M plus 8,000 output image tokens at $30 per 1M gives $0.2405 per image. A $10 budget buys about 41.6 images before taxes, minimums, retries, or failed generations.
How the words-per-dollar estimate works
The calculator turns output words into output tokens, prices one text request from input, cached input, and output token rates, then divides the budget by the per-request cost. Pages are output words divided by the selected words-per-page assumption.
How images are estimated
The image estimate uses GPT-Image-2 text input and image output token prices from the OpenAI pricing page. Image token count is an editable assumption because size, quality, and model accounting affect the final token count.
What this estimate excludes
The result excludes taxes, failed generations, retries, moderation calls, storage, vector search, web search, workflow platform tasks, staff review, and post-processing. Add a buffer for real production batches.
What to do next
Run a small sample, export usage logs, replace the token assumptions with actual medians, and set budget alerts before generating at scale.
Reference data used by the defaults
Topic
Reference value
Source
Date
Note
OpenAI GPT-5.5 text pricing
$5.00 input, $0.50 cached input, and $30.00 output per 1M tokens.
GPT-Image-2 lists image input at $8.00 per 1M tokens, cached image input at $2.00 per 1M tokens, image output at $30.00 per 1M tokens, and text input at $5.00 per 1M tokens.
Image token count depends on size, quality, and model accounting. The per-image token inputs are editable planning assumptions.
All prices and token-count assumptions should be replaced with current usage logs or provider calculator values before budget approval.
FAQ
Is this the same as the AI API Cost Calculator?
No. The AI API Cost Calculator starts with expected usage and estimates spend. This calculator starts with a fixed budget and estimates how much output that budget can buy.
Why does the words estimate ask for input tokens?
Even if you care about output words, every request usually has prompt, instruction, context, or tool tokens. Those input tokens consume budget before any generated words are returned.
How many words are in one output token?
The default 0.75 words per token is a planning ratio for English prose. Exact output varies by language, punctuation, code, formatting, and model tokenizer.
Why are image tokens editable?
OpenAI prices GPT-Image-2 images by token accounting, but the token count depends on image settings. Enter the token count from the provider calculator or your usage logs for current image sizes.
Does the estimate include failed calls or retries?
No. It shows clean budget capacity from the entered prices and token sizes. Add a retry buffer when prompts fail, users regenerate answers, or image outputs need multiple attempts.
What should I do before buying a large batch?
Run a small paid sample, check usage logs, replace the token assumptions with real medians, and set budget alerts or hard limits in the provider dashboard.
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