While the cost-per-token is decreasing as models become more efficient, this efficiency gain drives a massive increase in new use cases and overall consumption. This economic principle, Jevons Paradox, explains why total enterprise spending on model inference is skyrocketing, even as the unit cost falls.

It's counterintuitive, but using a more expensive, intelligent model like Opus 4.5 can be cheaper than smaller models. Because the smarter model is more efficient and requires fewer interactions to solve a problem, it ends up using fewer tokens overall, offsetting its higher per-token price.

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.

The initial approach to AI adoption was often "token maxing"—using as many tokens as possible under the assumption that more usage equals more value. A more sophisticated and sustainable strategy is "output maxing," which focuses on achieving the desired result while actively minimizing token consumption and cost.

The binary distinction between "reasoning" and "non-reasoning" models is becoming obsolete. The more critical metric is now "token efficiency"—a model's ability to use more tokens only when a task's difficulty requires it. This dynamic token usage is a key differentiator for cost and performance.

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.

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.

Popular AI coding benchmarks can be deceptive because they prioritize task completion over efficiency. A model that uses significantly more tokens and time to reach a solution is fundamentally inferior to one that delivers an elegant result faster, even if both complete the task.