MIT researchers looking at comparable data see costs dropping 5x to 10x annually. Once they strip out cheaper hardware and competitive pricing pressure, they put the actual gain in algorithmic efficiency at about 3x per year. Peak performance per run can actually get pricier, because newer reasoning models burn through a lot more compute per task.

Both studies are asking different things, though. Matching last year’s top-of-the-line capability? Dramatically cheaper. Running the current best model? Often significantly more per query.

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.

Epoch bases its analysis on five benchmarks spanning math, science, and logic puzzles. Because that’s a narrow sample, the organization calls its findings “reasonable but rough measurements based on the best available data.”

Algorithmic gains only explain part of the drop

Hans Gundlach and his MIT colleagues use pricing data from the comparison platform Artificial Analysis, covering April 2024 through November 2025, and evaluate far more models per test than previous research. Their numbers are lower than Epoch’s partly because newer reasoning models can throw extra test-time compute at hard problems, driving up the cost per correct answer even while per-token prices keep falling. Tokens are the text units providers bill for, and comparing them alone misses the full picture.

When MIT breaks down what’s pushing prices lower, cheaper hardware accounts for some of it, competition for more. The researchers control for competition by looking at open models separately. What’s left is the pure algorithmic efficiency gain, which comes out to about 3x per year. Epoch’s 13x figure is higher because it doesn’t strip out hardware and competition effects.

Better benchmark scores don’t always mean better efficiency

MIT also found that some performance gains simply come from spending more compute. A new model that beats its predecessor on GPQA Diamond looks like progress from the outside, but the authors estimate a big chunk of the improvement comes from using more processing power per question. It scores higher and costs more to run. Coding and math benchmarks show a smaller version of the same pattern.

Not all benchmark progress is efficiency progress. New models roll together better training, better data, better architecture, and more test-time compute. A single score blends all of that.

There’s also the “benchmaxxing” problem: AI companies could optimize for well-known tests, inflating scores without real-world payoff. Epoch tries to guard against this by including one test, “Mystery Game Puzzles,” based on a game that’s been kept secret. Costs drop slowest on that test, which fits the benchmaxxing theory but could just as easily reflect the task format or noise in the data.

Price alone won’t tell you which model to pick

Broad cost averages don’t help much when you’re choosing a model for a specific job. Platforms like Artificial Analysis rank models across quality, price, latency, context window, and output speed, and the cheapest option rarely wins on every dimension.

A low-cost model with high latency is useless for a real-time chatbot. A powerful reasoning model might be too slow for automated workflows. A pricier frontier model could still save money if it gets things right more often and cuts down on retries. None of that shows up in a simple price-per-token comparison. We dug into this token economics question in Frontier Radar #3.