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OpenAI’s o3 model falls short of its own benchmark claims

In this post:

  • OpenAI claimed that its o3 model could solve over 25% of FrontierMath problems, but new tests by Epoch AI reveal that the public version can solve about 10%.
  • ARC Prize and an OpenAI engineer confirm that the released model is a leaner and faster build tuned for chat usage.
  • Rival companies, including xAI and Meta, also face scrutiny for bloated benchmark results.

OpenAI’s newest LLM, o3, is facing scrutiny after independent tests found it solved a far fewer number of tough math problems than the company first claimed. 

When OpenAI unveiled o3 in December, executives said the model could answer “just over a fourth” of the problems in FrontierMath, a notoriously hard set of graduate‑level math puzzles.

The best competitor, they added, was stuck near 2%. “Today, all offerings out there have less than 2%,” Chief Research Officer Mark Chen said during o3 and o3 mini livestream. “We’re seeing, with o3 in aggressive test‑time compute settings, we’re able to get over 25%.”

TechCrunch reported that the result was obtained by OpenAI on a version of o3 that used more computing power than the model the company released last week.

On Friday, the research institute Epoch AI, which created FrontierMath, published its own score for the public o3.

Using an updated 290‑question edition of the benchmark, Epoch put the model at about 10%.

The result does match with a lower‑bound figure in OpenAI’s December technical paper, and Epoch cautioned that the discrepancy could be due to various reasons.

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“The difference between our results and OpenAI’s might be due to OpenAI evaluating with a more powerful internal scaffold, using more test‑time computing, or because those results were run on a different subset of FrontierMath,” Epoch wrote.

FrontierMath is designed to measure progress toward advanced mathematical reasoning. The December 2024 public set contained 180 problems, while the February 2025 private update expanded the pool to 290.

Shifts in the question list and the amount of computing power allowed at test time can cause large swings in reported percentages.

OpenAI confirmed the public o3 model uses less compute than the demo version

Evidence that the commercial o3 is lacking also came from tests by the ARC Prize Foundation, which tried an earlier, larger build. The public release “is a different model… tuned for chat/product use,” ARC Price Foundation posted on X, adding that “all released o3 compute tiers are smaller than the version we benchmarked.”

OpenAI employee Wenda Zhou offered a similar explanation during a livestream last week. The production system, he said, was “more optimized for real‑world use cases” and speed. “We’ve done [optimizations] to make the model more cost efficient [and] more useful in general,” Zhou said, while acknowledging possible benchmark “disparities.”

Two smaller models from the company, o3‑mini‑high and the newly announced o4‑mini, already beat o3 on FrontierMath, and OpenAI says a better o3‑pro variant will arrive in the coming weeks.

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Still, it shows how benchmark headlines can be misleading. In January, Epoch was criticized for delaying disclosure of OpenAI funding until after o3’s debut. More recently, Elon Musk’s startup xAI was accused of presenting charts that overstated the capabilities of its Grok 3 model.

Industry watchers say such benchmark controversies are becoming an occurrence in the AI industry as companies  race to capture headlines with new models.

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