Why is output so much more expensive?

Generating tokens is sequential and compute-bound where reading a prompt can be processed in parallel. Providers price that difference, typically at three to five times.

What should I optimise first?

Response length, usually, since output prices highest. After that, prompt caching for any stable prefix, which discounts repeated input heavily.

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LLM Token Cost Calculator

Model API cost from token volumes. Output tokens usually price three to five times input, so a small share of the tokens drives most of the bill, and shortening responses saves more than shortening prompts.

Also called: api token cost calculator, gpt cost calculator.

Monthly cost

$112,500.00

$112,500.00 a month for 100,000 requests: $50,000.00 on input and $62,500.00 on output. Output is 55.56% of the bill from 20% of the tokens, because it prices 5 times higher. Cost per request is $1.13. No prompt caching applied. A stable system prompt or shared context is usually worth caching.

Input cost

$50,000.00

Output cost

$62,500.00

Output share of cost

55.56%

Output share of tokens

20%

Output to input price ratio

Cost per request

$1.13

Saving from prompt caching

$0.00

Total tokens, millions

250

On prompt caching

No prompt caching applied. A stable system prompt or shared context is usually worth caching.

On prices

Prices are inputs, not constants. Model pricing changes often, so take the current rate from the provider rather than relying on a default here.

How this is calculated

Method and background

How this is calculated

Cost is requests times tokens times price per million, priced separately for input and output. The asymmetry is the useful insight: output typically costs several times input, so a response of 500 tokens can cost more than a prompt of 2,000. That inverts the usual optimisation instinct, which is to trim the prompt. Prompt caching, where a repeated prefix is billed at a large discount, is the other major lever and suits any workload with a stable system prompt or shared context.

output tokens typically price several times input, so a verbose response costs more than a long prompt

n: Requests
t: Tokens

Method and limits

What it assumes

Token counts you supply. Actual tokenisation varies by model and language.

What it deliberately does not model

A token is roughly four characters of English and considerably fewer for other scripts.
Cached input pricing and eligibility rules differ by provider.
Batch and asynchronous tiers often price lower and are not modelled here.

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Frequently asked questions

Why is output so much more expensive?: Generating tokens is sequential and compute-bound where reading a prompt can be processed in parallel. Providers price that difference, typically at three to five times.
What should I optimise first?: Response length, usually, since output prices highest. After that, prompt caching for any stable prefix, which discounts repeated input heavily.