Epoch AI reported that the cost of achieving a fixed performance level on select AI benchmarks has dropped sharply since 2023. The organization says costs are falling by about 47 percent per quarter on average, or about 13x per year.
The number reflects market prices for a fixed benchmark score, though, not pure algorithmic or architectural progress or even real-life productivity costs, which is a whole other story. MIT researchers looking at comparable data see costs dropping 5x to 10x annually.
"Matching last year's top-of-the-line capability? Dramatically cheaper," said Epoch AI. "Running the current best model? Often significantly more per query."
Both studies are asking different things, though. Matching o3's accuracy now costs a fraction of the price Epoch uses OpenAI's o3 as an example. In early 2025, o3 scored 75 percent on GPQA Diamond, a PhD-level science test, at an estimated 30 cents per question.
Eighteen months later, a GPT-5.6 family model hit the same score for four hundredths of a cent. Epoch says that's 1/725 of the original price. If cars dropped that fast, a 50,000-euro vehicle would cost less than 70 euros.
OpenAI launched the even cheaper GPT-6 Sol and Luna models just days ago, so the gap has likely widened further. Price per question for a fixed accuracy rate across multiple AI benchmarks over time. | Image: Epoch AI
Source: thedecoder
