A token is no longer a comparable unit of work.

2 models can solve roughly the same problem, yet 1 may need 5K tokens while another needs 50K+.

Add different tokenizers, hidden reasoning tokens, tool calls and retries, and "$ per 1M tokens" has no meaning for the workload.

There is an even bigger implication for agents. Token inefficiency compounds. A verbose output from step 1 often becomes input for step 2, then gets carried into step 3, step 4 and beyond.

So a model using 2x more tokens does not necessarily create only 2x more expense. It can also increase context size, generation latency, tool-call overhead and the cost of every later reasoning step. Token efficiency becomes much more valuable as workflows get longer.

The better economic measure is probably cost per successful task at a required quality level:

total model + reasoning + tool + retry cost ÷ successful completed tasks.

Token economy may be moving toward something similar to a semantic efficiency metric: how much useful work, intelligence or task completion you get from each dollar