Building abundant intelligence

AI infrastructure is not valuable because it is large. It is valuable because of what it makes possible: more capable intelligence, available to more people, at a lower cost.

That is how I think about abundance. It is both embedded in our mission—ensuring that artificial general intelligence benefits all of humanity—and in the economic engine that drives our business.

When the cost of useful intelligence falls, more work becomes worth doing. When models become more capable, that work creates more value. As adoption grows, we gain the revenue, real-world feedback, and visibility into demand to keep investing in the next generation of research and infrastructure.

Better intelligence drives broader adoption. Broader adoption supports more investment. More investment improves intelligence and efficiency. That is the cycle we are building.

The economics of abundance

Our recent pricing announcement shows how that cycle quickly benefits customers. Yesterday, we reduced the price of GPT‑5.6 Luna by 80 percent and GPT‑5.6 Terra by 20 percent. Luna now costs $0.20 per million input tokens and $1.20 per million output tokens; Terra costs $2 and $12, respectively.

For GPT‑5.6 Sol, Fast mode delivers up to 2.5 times the speed of standard processing at twice the price, with no change in intelligence.

These are not simply changes to a price list. They expand the range of work that becomes practical and give customers more flexibility to balance intelligence, speed, reliability, and cost.

The right question is not which model belongs to which task. It is how much intelligence the outcome demands for the required result, how quickly the intelligence is needed, and what the intelligence should cost to achieve. That balance may change several times within the same workflow.

Getting more from every unit of compute

Delivering that value requires more than building additional data centers. It requires making every unit of compute more productive.