Why Cost Per Token Is the Wrong AI Metric

Cost per token is an infrastructure metric. Cost per successful task is a business metric. Here's the one equation that connects them — and why it flips the model you should deploy.

Every model you can call — GPT, Gemini, Claude, Qwen, DeepSeek — is sold on cost per token and paid for on cost per successful task. Those are not the same number, and the gap between them is where AI budgets quietly die. Modern systems increasingly route each request to a different model by complexity instead of using one model for everything — so which number you optimize is a live architectural decision.

The reason: a frontier model is priced against labor substitution, not compute. Its anchor isn't a GPU-hour — it's an engineer's hourly rate. So once output can be wrongin a way a human must fix, the token bill becomes a rounding error next to...

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