Intelligence Per Dollar

Yesterday Microsoft added a new metric to a model release card, one that will likely become a standard.¹

Average token usage.

In the first row, the Microsoft model hits 71.6 on SWE-Bench Verified using about a third of the tokens Claude Haiku 4.5 burns.

Benchmarks are now measured on two different dimensions, the overall performance & the cost to achieve that intelligence.

This is yet another sign that the era of subsidies², tokenmaxxing³, & all-out performance for many use cases is over.

Even the most valuable companies in the world cannot afford state-of-the-art intelligence for every conceivable use case.⁴ Uber capped employee AI spending after blowing through its budget in four months.⁵ Salesforce is spending $300M on Anthropic tokens & has frozen engineering hires.⁶

This new dual benchmark answers the buyer’s only question : what is my intelligence per dollar?

Artificial Analysis already benchmarks this.⁷ GPT 5.5 & Claude Opus 4.8 land within a point of each other on the Intelligence Index, around 60. Running the index costs $3,357 on GPT 5.5 & $4,685 on Opus 4.8. Same answer, 40% more expensive.

Model companies must now compete on both dimensions. The application layer will compete one level up, on dollars per outcome, what a closed ticket, a shipped PR, or a resolved support case actually costs.

Every layer in the stack now has to price the same way the customer thinks : per result, not per token.