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What you should know about this indicator

  • This indicator shows the share of FrontierMath problems that AI models solve correctly, based on Epoch AI's evaluation.
  • FrontierMath is a set of 338 original math problems written by experts, covering many areas of advanced mathematics. Many problems are difficult enough that human specialists might need hours or days to solve them.
  • The benchmark has four difficulty tiers. This indicator shows accuracy on Tiers 1–3 (295 problems). Tier 4 contains 43 exceptionally difficult problems and is not included here.
  • In June 2026, Epoch AI reissued FrontierMath after addressing errors in 42% of its problems. This indicator uses the corrected problem set. Scores on it are not comparable with scores on the original set, where the flawed problems held every model well below 100%; models evaluated on both scored around 12 percentage points higher on the corrected set.
  • Scoring is all-or-nothing: models get 1 point for a correct final answer and 0 for anything else, with no partial credit. Models submit their answers as Python code and can use Python while working on problems. This means scores reflect math ability with access to computational tools, not just pen-and-paper reasoning.
  • Only 12 of the problems are publicly available: 10 from Tiers 1–3 and 2 from Tier 4. They are published so researchers can inspect how evaluations work, not to report scores.
  • FrontierMath was developed by Epoch AI with funding from OpenAI, whose GPT models are among those evaluated on this benchmark. OpenAI has exclusive access to a subset of the problems.

How is this data described by its producer?

FrontierMath is a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. The questions cover most major branches of modern mathematics – from computationally intensive problems in number theory and real analysis to abstract questions in algebraic geometry and category theory. Solving a typical problem requires multiple hours of effort from a researcher in the relevant branch of mathematics, and for the upper end questions, multiple days.

On 2026-06-12, we released a major update, addressing errors in 42% of problems. Following this update, the full FrontierMath dataset consists of 338 problems. This is split into a base set of 295 problems, which we call Tiers 1-3, and an expansion set of 43 exceptionally difficult problems, which we call Tier 4. We have made twelve problems public: ten from Tiers 1-3 and two from Tier 4. Unless stated otherwise, all the numbers on this hub correspond to evaluations on the private sets. You can find the public problems here.

FrontierMath was developed with funding from OpenAI, who has exclusive access to a subset of the benchmark.

Share of FrontierMath problems solved correctly by AI models
FrontierMath evaluates models on 295 difficult, research-level problems in advanced mathematics (Tiers 1–3), which can take expert mathematicians hours or days to work through.
Source
Epoch AI (2026) – with minor processing by Our World in Data
Last updated
September 7, 2026
Next expected update
October 2026
Unit
%

Sources and processing

This data is based on the following sources

Epoch AI – Epoch AI Benchmark Data

Comprehensive collection of AI benchmark datasets from Epoch AI, including FrontierMath and other performance benchmarks.

Retrieved on
September 4, 2026
Retrieved from
https://epoch.ai/benchmarks
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Epoch AI, ‘AI Benchmarking Hub’. Published online at epoch.ai. Retrieved from ‘https://epoch.ai/benchmarks’ [online resource].

Comprehensive collection of AI benchmark datasets from Epoch AI, including FrontierMath and other performance benchmarks.

Retrieved on
September 4, 2026
Retrieved from
https://epoch.ai/benchmarks
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Epoch AI, ‘AI Benchmarking Hub’. Published online at epoch.ai. Retrieved from ‘https://epoch.ai/benchmarks’ [online resource].

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Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Share of FrontierMath problems solved correctly by AI models”, part of the following publication: Edouard Mathieu, Charlie Giattino, Veronika Samborska, and Max Roser (2023) - “Artificial Intelligence”. Data adapted from Epoch AI. Retrieved from https://archive.ourworldindata.org/20260907-172109/grapher/ai-frontiermath-over-time.html [online resource] (archived on September 7, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Epoch AI (2026) – with minor processing by Our World in Data

Full citation

Epoch AI (2026) – with minor processing by Our World in Data. “Share of FrontierMath problems solved correctly by AI models” [dataset]. Epoch AI, “Epoch AI Benchmark Data” [original data]. Retrieved September 17, 2026 from https://archive.ourworldindata.org/20260907-172109/grapher/ai-frontiermath-over-time.html (archived on September 7, 2026).

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Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/ai-frontiermath-over-time.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/ai-frontiermath-over-time.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/ai-frontiermath-over-time.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/ai-frontiermath-over-time.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/ai-frontiermath-over-time.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/ai-frontiermath-over-time.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear