When evaluating AI agents, the total cost of task completion is what matters. A model with a higher per-token cost can be more economical if it resolves a user's query in fewer turns than a cheaper, less capable model. This makes "number of turns" a primary efficiency metric.

Newer AI models may have low per-token prices but are often "token hungry," requiring more tokens to complete a task. This can make them more expensive overall. The true measure of economic viability is the final cost-per-task, not the misleading per-token price.

The total cost of an AI task depends on the outcome quality. A high-quality model might use more expensive tokens but achieve the result faster and with fewer attempts, making the overall system cost lower than a cheaper, less effective model.

OpenAI's GPT-5.5 is more expensive per token, but a new evaluation framework is emerging. The key metric isn't raw cost, but the model's efficiency in solving a problem. This 'intelligence per dollar' reframes cost analysis around performance and compute, where more expensive models can be cheaper overall if they solve tasks more efficiently.

Evaluating AI models on cost-per-token is misleading because it ignores the hidden cost of human labor to fix failures. The true 'cost per successful task' is a business metric that accounts for both the API invoice and the payroll expense for rework, revealing a more accurate total cost of ownership.

A model with a low per-token price can be more expensive if it's inefficient, verbose, or requires multiple attempts ('overthinking'). The actual invoice depends on the total tokens needed to complete a task, making token efficiency a hidden multiplier that savvy enterprises are now tracking to determine the true cost.

In complex, multi-step tasks, overall cost is determined by tokens per turn and the total number of turns. A more intelligent, expensive model can be cheaper overall if it solves a problem in two turns, while a cheaper model might take ten turns, accumulating higher total costs. Future benchmarks must measure this turn efficiency.

Focusing on token pricing is misleading. A more powerful model may be more expensive per token but significantly cheaper per task because its higher efficiency requires fewer prompts and iterations to achieve a final result. The correct way to measure cost-effectiveness is by the total cost to complete a job, not the price of the raw material.

Sticker price per token is a misleading metric for AI models. A cheaper model may require more retries or reasoning, making it more expensive overall. The true metric is 'cost per accepted task,' which accounts for total resources needed to get a reliable, usable result, providing a true apples-to-apples comparison.