OpenAI proactively manages spending during prompt usage: controlling costs with 5 steps for managing AI investment

From GPT-4 to GPT-5.4, the price for 1M Tokens dropped by 97%, GPT 5.6 output Tokens decreased by 54%, and task time decreased by 57%
But the price of Tokens itself doesn’t mean AI is creating value for you.
1. Fully understand your AI usage and spending
Know what you’re doing, what the model is being used for, where Tokens spend is going. Get a complete view of which processes (or which model) Tokens should go to, and where you need to set limits;
2. Evaluate model efficiency by ROI from outcomes
It’s not about blindly using the top-tier model (now GPT-5.6 Sol) or assuming it’s always right (if money is no object, it doesn’t matter). Switch between models with different price points based on the actual task and acceptable workflows. Evaluate the model’s role across the whole project based on results, and then reconcile the overall cost to uphold the low-risk principle;
3. Governance for advanced workflows before scaling up
Deploy access permissions, data retention policies, context size, and so on in advance. This helps with cost accounting and reducing risk;
4. Increase investment in workflows that compound returns
More funds (Tokens) should be put into improving daily productivity and refining repeatable workflows;
5. Match verified needs
If a workflow has already proven its value (improving efficiency or monetization, etc.), expand that validated need by extending all AI capabilities (Chat, Coding, Agent, etc.) through proprietary data and the like, and use AI to reduce costs or enhance the workflow.