AI & LLM cost
Cost per 1M tokens
The standard unit LLM providers use to publish prices: the dollar cost of one million input or output tokens. A single token is too small a unit to price.
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Definition
A single token is worth a small fraction of a cent. So providers publish rate cards in dollars per million tokens, usually as separate input and output rates. Comparing models on this number alone is a useful first filter. It is not the full picture. A model with a lower per-token rate but less capability can lose to a pricier model. The pricier one may need fewer follow-up calls, or give a usable answer on the first try.
The number that predicts your bill is this rate times your real token volume, per model and per workload. A normalized cost view has to show that number to be useful.
Where it shows up
Cost per 1M tokens is the unit that every provider publishes on its pricing page. Each model lists a separate input rate and output rate. A usage-based invoice applies that rate to the total token counts reported across the billing period, model by model.
What makes it expensive
Teams pick a model purely on its per-token rate. They do not check how many follow-up calls or retries it takes to get a usable answer. The rate card prices a token. The bill prices a finished task. Those two rankings are not always in the same order.
Related
Token spend moves every day.
Model prices and rate limits change between invoices. CostMon tracks your AI spend daily, so a change shows up before the bill does.