Intelligence vs Cost Per Token

Intelligence vs Cost Per Token

When choosing an AI model, there is always a trade off between intelligence and cost. The most capable models are expensive to run, while cheaper models are less capable. Understanding this relationship helps you make smart decisions about which model to use for each task.

Cost per token is the fundamental metric. If a model costs $10 per million tokens and generates 1000 tokens per response, each response costs one cent. A more capable model might cost $100 per million tokens, making each response ten cents. For a casual user, this difference is negligible. For a business processing millions of requests per day, it adds up quickly.

The relationship between cost and capability is not linear. The best models are dramatically more expensive than slightly worse ones. GPT-4 might be 10 to 20 times more expensive than GPT-4o mini, but it is not 20 times smarter. For many tasks, the cheaper model performs almost as well. The expensive model only pulls ahead on complex reasoning, creative tasks, or tasks requiring precise instruction following.

This is where model routing becomes valuable. The idea is to use a cheap model for simple tasks and a expensive model for complex ones. A classifier determines the difficulty of each request and routes it accordingly. This can reduce costs by 80 to 90 percent while maintaining quality on the tasks that matter.

The trend is toward lower costs over time. As models become more efficient and competition increases, the cost per token is dropping rapidly. A model that cost $100 per million tokens a year ago might cost $10 today. Running models locally also changes the equation entirely: there is no per token cost, just the fixed cost of the hardware. For high volume usage, local inference is almost always cheaper than API calls.