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Highest MMLU score achieved by an AI model, by country of origin

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Artificial Intelligence

What you should know about this indicator

  • This indicator shows the highest MMLU score achieved so far by a model from a given country or region.
  • MMLU (Massive Multitask Language Understanding) is a benchmark that tests AI models across 57 subjects, from school-level science and mathematics to professional fields such as law and medicine.
  • Scores show the share of multiple-choice questions a model answered correctly. Some models are tested with example questions before answering new questions, while others are not.
  • Models are grouped by country or region using information on where the developer is based.
  • France, the United Kingdom, and Germany are grouped into a single "Europe" category.
  • Data are compiled by Epoch AI from published papers, official leaderboards, and other primary sources.

How is this data described by its producer?

MMLU consists of four-choice questions spanning humanities, STEM, social sciences, and professional domains. Many questions require recall of domain facts, application of definitions, or light reasoning under time constraints—skills analogous to standardized testing. Due to its breadth and stability, MMLU is frequently used as a headline indicator of general knowledge in model reports. Sub-scores by discipline can reveal strengths and weaknesses across subject areas.

Highest MMLU score achieved by an AI model, by country of origin
Highest MMLU score achieved by a model from a given country or region, shown whenever a new record was set. MMLU is a multitask benchmark made up of exam-style questions across dozens of academic and professional subjects.
Source
Epoch AI (2026) – with major processing by Our World in Data
Last updated
January 30, 2026
Next expected update
May 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
March 8, 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]. Accessed 30 Jan 2026.

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

Retrieved on
March 8, 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]. Accessed 30 Jan 2026.

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

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  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

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: Highest MMLU score achieved by an AI model, by country of origin”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska, and Max Roser (2023) - “Artificial Intelligence”. Data adapted from Epoch AI. Retrieved from https://archive.ourworldindata.org/20260326-115107/grapher/highest-mmlu-by-country.html [online resource] (archived on March 26, 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 major processing by Our World in Data

Full citation

Epoch AI (2026) – with major processing by Our World in Data. “Highest MMLU score achieved by an AI model, by country of origin” [dataset]. Epoch AI, “Epoch AI Benchmark Data” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260326-115107/grapher/highest-mmlu-by-country.html (archived on March 26, 2026).

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https://ourworldindata.org/grapher/highest-mmlu-by-country.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/highest-mmlu-by-country.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/highest-mmlu-by-country.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/highest-mmlu-by-country.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/highest-mmlu-by-country.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/highest-mmlu-by-country.csv?v=1&csvType=full&useColumnShortNames=false")

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