A scorecard for the AI age

What happened

The question I hear from CFOs everywhere is simple: how do we get more value from our AI spend? For years, the market measured the success of software through adoption: seats purchased, users active, licenses renewed. Understanding the value of AI demands a more powerful measure: work accomplished.

The basic economic question facing CFOs and other business leaders is whether the value of the work AI completes grows faster than the cost of producing it. Answering that question requires looking more deeply than a metric such as cost per token. A lower-cost model may have cheaper tokens, but getting great results may require more attempts, more time, or more human review.

A more capable model may have more expensive tokens, but complete the same task in one pass. What matters is the full cost of producing a successful outcome, measured against the value that outcome creates. ” This metric answers four key questions: How many customer issues did AI help resolve? How many code changes did it help ship? How many contracts did it review?

How much time did it give back to people? How many decisions improved because the right context was available at the right moment? Tokens create value when they transform into work people can use. As models become more capable, they can take on longer and more complex tasks: maintaining context, reasoning through multiple steps, working across tools, and adapting as they go.

The best place to begin is with one workflow. Define what “done” means and measure that outcome in the system where the work happens. For a support team, “done” might mean a customer issue resolved. For an engineering team, it might mean a code change that passes its tests. For a legal team, it might mean a contract reviewed accurately and on time.

Consider a finance team preparing for a forecast review. Much of the work happens before a final decision is made: finding the latest forecast, moving data into Excel or Sheets, identifying changes, reconciling tabs, rebuilding slides, and checking that everything adds up perfectly. ChatGPT Work can take on much of that process, giving the team more time to focus on the questions that matter: What changed? Why?

Those more complex tasks can require more compute, but they can create much more value. At the model level, cost per successful task depends on price, the amount of compute used, and the likelihood of reaching the right result. For a business, the full cost also includes employee time, human review, retries, and rework.

This is why the lowest price per token does not always produce the lowest cost per outcome. A frontier model may deliver the best value even for a routine request if it produces the right answer in one pass, reducing retries, latency, review, and total compute. A tiered model family gives customers more ways to optimize this equation.

Why it matters

What should we do next? That is useful intelligence per dollar in practice. More work gets completed, faster, while people spend more of their time applying judgment, creativity, and expertise. The next question is what it costs to complete that work well. AI tasks vary widely. A quick answer may require little compute. A coding, research, or financial workflow may involve deeper reasoning, tool use, and many actions.

What to watch

6, which we released last week, has three tiers: Sol is our flagship; Terra balances performance and cost; Luna is our fastest and most affordable model. These tiers provide useful starting points. The economics of the full task should ultimately determine the right model.

A customer might use Luna for a fast, high-volume workflow, Terra for work requiring greater depth, or Sol when stronger reasoning delivers the best result with fewer attempts. 6 to get more useful work from every token. 6 Sol with max reasoning set a new state of the art while usin