What are LLM token costs?
LLM token costs are what a company pays for the text a language model reads and writes, billed per million tokens and charged separately for input and output. They are the running cost of using AI at work, and unlike a software subscription they scale with how much the tool is actually used , which is why they are the line that surprises finance teams in the second year rather than the first.
How it works
- 01Every request is billed on the tokens going in , the instruction, the conversation so far, any attached context , and the tokens coming out.
- 02Output is typically priced several times higher than input, but in workloads with company context attached the input is usually the larger volume.
- 03The bill therefore grows with three things independently: how many people use it, how much context each request carries, and which model handles it.
What LLM token costs are not
Falling model prices do not automatically lower the bill. Per-token prices have dropped sharply, and spend has still risen at most companies, because cheaper tokens get used for more things and longer contexts. A second mistake is reading the per-token price of a frontier model as the cost of the work: a task that fits a smaller model costs what the smaller model charges, and the difference between those two numbers is usually larger than the difference between any two frontier providers.
How Flocta uses it
Flocta reduces token cost in software rather than by giving a weaker answer: semantic caching reuses work already done, context compression sends only what the task needs, and routing hands each request to the cheapest model that still holds the required quality. Up to 35 percent lower cost at the same output quality, depending on the workload.
Questions people ask
How do you reduce LLM token costs without losing quality?
By attacking the three things that drive the bill rather than the price per token: stop paying twice for work already done, stop sending context the task does not use, and stop paying frontier rates for tasks a cheaper model handles at the same measured quality.
Why did our AI bill rise even though model prices fell?
Because cheaper tokens get used for more work and longer contexts. Per-token price is only one of three multipliers; the other two , how much context each request carries and which model handles it , usually move in the wrong direction as adoption grows.
Flocta runs all three of these underneath the assistant your team already uses.
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