Are we measuring AI cost wrong by obsessing over token price?

Token price is easy to compare.

That is why it gets overused.

But for real AI work, the better metric is:

What did it cost to finish the task?

Research.
Writing.
Code.
Images.
Voice.
Automation.

If a workflow needs twice the tokens to get to the same usable result, the cheaper token may not actually be cheaper.

We are building TokenFans around this idea:

• shared credits
• one workflow layer
• model flexibility
• cost per finished task

For people using AI daily:

Do you track spend by request, model, project, session, finished task, or basically vibes?