AI token pricing charges customers per token of model usage, where a token is a chunk of text (roughly four characters) the model reads or writes. Cost scales with consumption instead of a flat per-seat fee.
AI token pricing emerged because the cost of running a model scales with how much text it processes, not with how many people use the product. Providers meter tokens and charge for them directly, so a customer who sends one prompt a day pays less than one who streams thousands. It is the dominant pricing model for large language model APIs.
How AI token pricing works
AI token pricing meters every token a model consumes and converts it into a charge using a per-token rate. It is a specific form of usage-based billing where the metered unit is the token. A token is roughly four characters or three-quarters of an English word, so “billing” is one token and a 500-word email is about 650. Providers count two streams separately: input tokens (the prompt and context sent to the model) and output tokens (the text the model generates). Output is usually priced higher than input because generation is more compute-intensive.
Rates are quoted per million tokens, and a request’s cost is (input × input_rate) + (output × output_rate). Because the meter runs on consumption, spend tracks usage in real time rather than resetting to a fixed number each month.
AI token pricing examples
Every major model API uses token pricing with a split input/output rate. OpenAI prices its GPT models per one million input and output tokens, with output billed higher than input. Anthropic prices Claude models the same way and discounts cached input tokens reused across requests. Google’s Gemini API also meters per token, with separate rates by model tier.
A product that wraps one of these APIs inherits a variable cost on every request, which is why many AI products pass token pricing through to their own customers rather than absorbing it into a flat fee.
AI token pricing vs Seat-based pricing
| AI token pricing | Seat-based pricing | |
|---|---|---|
| Charge basis | Tokens consumed | Number of users |
| Cost alignment | Tracks provider cost per request | Independent of usage |
| Revenue at low usage | Low (pay for what you use) | Fixed per seat |
| Margin risk | Low; price follows cost | High; a heavy user on one seat can run negative margin |
| Best for | Usage that varies widely per customer | Predictable, headcount-driven collaboration tools |
Benefits & when to use it
AI token pricing fits products whose cost per customer is variable and tied to model usage. It aligns revenue with the underlying provider bill, so a customer who uses more pays more and margin holds, and a light user is not forced into a large seat fee before trying the product.
It is a weaker fit when usage is predictable and uniform, or when the product’s value is collaboration rather than computation. Token pricing is also hard for customers to forecast, which is why many products pair it with prepaid credits, spend caps, or a hybrid plan that bundles a token allowance into a fixed monthly fee.
FAQ
What is a token in AI pricing?
A token is the unit a language model reads and writes, roughly four characters or three-quarters of an English word. Providers split text into tokens before the model runs and charge per token, counting input (the prompt) and output (the generated text) separately.
Why is output priced higher than input?
Generating text is more compute-intensive than reading it, so providers charge a higher rate for output tokens than for input tokens. A request that produces a long response therefore costs more than one that sends a long prompt and gets a short answer.
How is AI token pricing different from usage-based pricing?
Token pricing is a specific form of usage-based pricing where the metered unit is the token. Usage-based pricing is the broader model that charges by any consumed unit, such as API calls, GPU hours, or documents processed.
How do products forecast and cap token costs?
Most products meter tokens in real time and apply spend caps, prepaid credit balances, or per-customer limits so one heavy user cannot run unbounded cost. Real-time authorization, which checks a balance before each request, enforces those caps before the spend happens rather than after.
How Credyt handles AI token pricing
Credyt is built for token-priced products. Each customer gets a multi-asset wallet that holds tokens or any custom unit natively, and Credyt authorizes every usage event against the balance before the request runs, then debits it the moment the event is recorded. Customers see their live balance and top up themselves in the drop-in portal, so token spend is controlled before it happens, not reconciled at month end. Explore Credyt →