From Capability to Utility
How we’re thinking about things
Hi
Welcome (back) to The Prompt. We’re recapping the big updates of the week, what’s top of mind for us, and how everyday people are using our tools:
Sam was in DC to preview the capabilities of our upcoming models.
Thanks to efficiency gains, we were able to cut the prices of two of our models in the interest of making better AI accessible for more people.
Reactions to our new voice model got us thinking about a future in which we’ll have a more natural, intuitive connection with the technology we use every day.
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[Perspective] The Value Frontier, Part 2
We’ve shared many thoughts recently about how AI models should be useful in the everyday world, including here in The Prompt and from our CFO Sarah Friar. For our new EOW issue, we’re bringing those ideas together.
We see the frontier increasingly as being about not just raw output, but how much useful work an AI model can do, how quickly and dependably it can do it, and what it costs. What we think of as useful intelligence per dollar.
GPT-5.6 shows what that looks like. We built it as a family for different points on the price-performance curve: Sol for the hardest problems, Terra for everyday work, and Luna for fast, high-volume workloads. Our new price reductions push that curve further, including an 80% cut for Luna and 20% for Terra, while Fast mode for Sol can deliver up to 2.5x faster performance.
This changes the economics of what people can build. Luna can now deliver performance comparable to models that were at the frontier just a year ago – at a fraction of the cost and many times the speed. On professional work measured by Agents’ Last Exam, Luna outperforms Fable 5 at an estimated cost per task nearly 99% lower.
But price alone isn’t value. A cheaper model that needs several attempts or substantial human correction can ultimately cost more than a more capable model that succeeds in one pass. The better measure is the full cost per successful outcome, and whether that outcome is dependable enough to become part of how work gets done.
The bigger story is how those economics are possible. Efficiency comes from improvements across the entire stack: models, infrastructure, inference, routing, caching, context management, and the systems that allow agents to use tools. Each improvement means getting more useful work from the same compute.
Increasingly, AI itself is accelerating those improvements. GPT-5.6 Sol has helped OpenAI engineers analyze production traffic, improve routing, optimize kernels and tune workloads. Together with broader engineering advances, work involving Sol contributed to a 20% reduction in end-to-end serving costs, while improvements to speculative decoding increased token-generation efficiency by more than 15%.
That creates a powerful flywheel: better intelligence produces greater efficiency >> greater efficiency makes intelligence cheaper and more abundant >> cheaper, more dependable intelligence can be used for more things. Each dollar and each unit of compute can accomplish more useful work.
This is the value frontier.
The next phase of AI won’t be defined by a benchmark score alone. The better questions are: Is AI completing work that matters? What does each successful task cost? Can people depend on the result? And does each AI dollar produce more value as usage grows?
The goal isn’t simply better AI. It’s making better AI useful and affordable for everyone.
[Product] Voice: the next form factor
So much of how we use technology today still requires us to stop what we’re doing, pull out a device, open an app, and type to a screen.
Voice changes all of that.
We’re biased, of course, but what interests us most in the reactions to our new voice model is the feedback about both how good it sounds and how natural it feels – how you can interrupt it, go back and forth, or change direction mid-thought, and it keeps up with you. Much less like using a piece of software, much more like just talking.
Voice can make AI fit more naturally into the way we live and communicate, rather than asking us to make our behavior fit the technology. We’re so used to hands on keyboards and eyes on screens that we don’t even think about how much of our day we spend that way — or how much we can’t do while we’re doing it.
That’s part of what makes voice so interesting. When you don’t need to hold a phone or type, you can use AI while your hands are doing something else: cooking dinner, fixing something around the house, working in a lab, stocking a shelf, tending a garden, sketching an idea, or caring for a child. A mechanic could talk through a repair without putting down a tool. A nurse could get information while moving through a hospital. A small business owner could work through tomorrow’s schedule while closing up for the night. The interface no longer has to compete with the task.
Liberating people from the keyboard-and-screen interface can make AI useful to a much broader group of people across a much broader expanse of their day, including people who would never think of themselves as power users today. It also changes where technology can be useful: not just at a desk or with a phone in your hand, but alongside you as you move through the physical world.
As the models get better and the interaction gets more natural, the interface will start to fade. You’re not thinking about prompting or operating a product. Instead, you’re just asking a question, thinking out loud, or getting help with whatever you’re doing.
That’s a pretty profound shift. The way we’ll interface with technology in the future will be more natural and intuitive – allowing us to focus on everything else.
[Weekly wrap] A team of agents for every worker
The next phase of AI will be defined not only by what a model can do on its own, but by what people can accomplish with teams of AI agents working on their behalf. That was the message that our CEO Sam Altman, Chief Strategy Officer Jason Kwon, and Noam Brown, one of our top researchers, brought to meetings this week on Capitol Hill, at the White House and with the executive branch, and with top economists.
Sam, Jason and Noam previewed OpenAI’s newest models to show how workers across a wide variety of fields and professions can oversee teams of agents that are working together on a larger assignment. With the economists, our Chief Economist Ronnie Chatterji noted that some of the most prolific users of ChatGPT Work inside OpenAI are on the non-technical teams.
Reactions to our presentations were similar: audiences were excited by what the AI advances mean for science and mathematics, but also demanded that the technology benefit business owners, workers, and people in their day to day.
We expect AI to give people greater reach and agency, helping them pursue ideas and projects that have required more time or resources than they’ve had, while making human judgment, creativity, and direction even more valuable. The opportunity is to give individual workers, small businesses and entrepreneurs the capabilities of an entire team, helping more people bring their ideas to life.
Also this past week:
Accelerating scientific discovery with ChatGPT for Academic Researchers: We launched ChatGPT for Academic Researchers, providing free frontier-model access to 100,000 scientists, mathematicians, and engineers at selected institutions through 2027. The initiative begins with 10,000 researchers this summer and is part of a bigger commitment of over $250 million to support external scientific research and discovery.
How AI is expanding what people do at work: Our first Work at the Frontier report finds that 43.5% of occupation-specific ChatGPT messages involve tasks traditionally associated with a different occupation, suggesting AI is already helping people take on work that once required a specialist hand-off.
Use cases we love: ChatGPT is helping you search for apartments, complete school paperwork for your children, and manage grocery lists. Also, meet the pastor in Washington, DC who is using ChatGPT to brainstorm his sermons and make Scripture more accessible to his young congregants, as well as the young couple Kiley and Cole who used ChatGPT to gain clarity – and a more hopeful prognosis – from Kiley’s biopsy records.
[Disclosure]
Graphics created by Base Three using ChatGPT.