Key Takeaways
- OpenAI introduces a ‘Useful Intelligence per Dollar’ metric focusing on work, cost, dependability, and scale.
- GPT-5.6 Sol outperforms Claude Fable 5 on coding and planning tasks at lower cost per task.
- The framework shifts enterprise AI evaluation from token prices to outcome-based value.
OpenAI Unveils a Four-Part Scorecard for AI’s Real Economic Value
OpenAI has published a new framework for measuring the economic value of artificial intelligence, moving beyond simplistic metrics like cost per token. The proposal, titled ‘A Scorecard for the AI Age,’ introduces a four-part metric called ‘Useful Intelligence per Dollar’ designed to help CFOs and business leaders evaluate AI investments by focusing on work accomplished rather than usage statistics.
Table of Contents
The Four Metrics of Useful Intelligence per Dollar
The framework addresses the basic question CFOs are asking: does the value of the work AI completes grow faster than the cost of producing it? OpenAI argues that lower token costs don’t guarantee lower cost per successful outcome, because cheaper tokens may require more attempts and human review.
1. How Much Useful Work Gets Done?
This metric starts by defining what ‘done’ means for each workflow. For a support team, it’s a resolved customer issue. For engineering, it’s a code change that passes tests. For legal, it’s a contract reviewed accurately and on time.
OpenAI gives the example of a finance team preparing for a forecast review. AI can handle the grunt work of finding data, reconciling tabs, and rebuilding slides, freeing humans to focus on strategic questions. This is useful intelligence per dollar in practice: more work completed, faster, while people apply judgment.
2. What Does a Successful Task Actually Cost?
Cost per successful task includes compute, employee time, retries, and rework. OpenAI emphasizes that the lowest price per token does not always produce the lowest cost per outcome. A frontier model may deliver better value if it gets the right answer in one pass.
The company’s tiered model family — GPT-5.6 Sol, Terra, and Luna — is designed to optimize this equation. Sol is the flagship for deep reasoning, Terra balances performance and cost, and Luna is the fastest and most affordable. The economics of the full task should determine the choice.
3. How Often Does AI Get the Work Right?
Dependability has direct economic value. When results are accurate and require minimal correction, successful tasks cost less. OpenAI suggests tracking three outcomes: ‘Ready to use,’ ‘Needs correction,’ and ‘Needs escalation.’
This goes beyond model accuracy. It shows whether AI is genuinely reducing the work involved. Before AI moves from drafting to taking action, organizations must define data access, system permissions, and approval boundaries.
4. Does Each AI Dollar Buy More Work as Usage Grows?
The final measure is whether economics improve at scale. Companies should track the same workflow over time: number of tasks meeting the quality bar, total cost, and cost per successful task. If completed work grows faster than total cost while quality holds, each AI dollar is producing more value.
Better models, efficient inference, purpose-built hardware, and smarter routing all compound. OpenAI brings these elements together through one platform, so improvements in one layer benefit every product.
How GPT-5.6 Sol Stacks Up Against Claude Fable 5
OpenAI’s scorecard arrives as the market sees fierce competition between frontier models. Real-world benchmarks from Lenny’s Newsletter show that GPT-5.6 Sol with max reasoning achieved a state-of-the-art score of 72.7% on the Artificial Analysis Coding Agent Index, beating Claude Fable 5’s 69.9% while using 54% fewer output tokens. This translates to an estimated 36.2% lower API cost per successful task.
On Reddit, users have noted that Sol tends to be faster and cheaper per task at high throughput, while Claude Fable 5’s more verbose responses can increase token costs. However, some prefer Fable for managerial tasks where explainability and detail are valued. The cost difference is stark: Fable is priced at $10 per million input tokens and $50 per million output, while Sol’s pricing is comparable to GPT-5.5, which is more competitive.
These comparisons underscore OpenAI’s point: cost per token is misleading. As Composio notes, Fable is priced at $10 per million input tokens and $50 per million output, while Sol’s pricing is comparable to GPT-5.5, making the cost per successful task a more accurate lens for procurement decisions. A task that requires multiple attempts with a cheaper model may ultimately cost more than a single pass with a more capable one.
The Bottom Line for Enterprise AI Adoption
OpenAI’s scorecard gives CFOs the vocabulary and metrics to assess AI investments beyond hype. By focusing on useful work, cost per task, dependability, and value at scale, it aligns AI evaluation with business outcomes.
Combined with real-world benchmarks showing GPT-5.6 Sol’s efficiency gains, the framework suggests that enterprises should prioritize outcome-based metrics when evaluating models. As OpenAI puts it, capability earns first use, but dependability makes AI part of how work gets done.
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Frequently Asked Questions
What is ‘Useful Intelligence per Dollar’?
It’s a four-part metric introduced by OpenAI to measure AI’s economic value by focusing on work accomplished, not just token costs. The four components are: useful work completed, cost per successful task, dependability (accuracy), and value at scale (improving economics as usage grows).
How does cost per successful task differ from cost per token?
Cost per token only measures the price of generating tokens, ignoring that cheaper models may require more attempts and human review. Cost per successful task includes compute, employee time, retries, and rework, providing a more accurate picture of true value.
Which model performs better: GPT-5.6 Sol or Claude Fable 5?
According to benchmarks from Lenny’s Newsletter, GPT-5.6 Sol with max reasoning scored 72.7% on the Artificial Analysis Coding Agent Index, beating Claude Fable 5’s 69.9% while using 54% fewer output tokens, leading to an estimated 36.2% lower API cost per successful task. However, some users prefer Fable for managerial tasks requiring detail.
What are the three tiers of OpenAI’s model family?
OpenAI offers GPT-5.6 Sol (flagship for deep reasoning), Terra (balance of performance and cost), and Luna (fastest and most affordable). The choice depends on the economics of the full task.
How should enterprises track AI dependability?
OpenAI suggests tracking three outcomes per task: ‘Ready to use’ (no correction), ‘Needs correction’ (minor fixes), and ‘Needs escalation’ (requires human intervention). This goes beyond model accuracy to measure whether AI actually reduces workload.
What is the value at scale metric?
It measures whether each AI dollar buys more work as usage grows. Companies should track number of successful tasks, total cost, and cost per task over time. If completed work grows faster than total cost while quality holds, value is compounding.
Why is cost per token misleading for AI procurement?
Because a task that requires multiple attempts with a cheaper model may cost more than a single pass with a more capable model. The scorecard’s cost per successful task provides a more accurate lens for comparing models.
