Multilingual Benchmark

MGSM

Multilingual Grade School Math - GSM8K translated to 10 languages.

Category

Multilingual

Score type

percent

Scale

100%

higher is better

Models tested

5

What is MGSM?

MGSM (Multilingual Grade School Math) is GSM8K translated into about ten other languages. It shows whether a model's math ability survives when the problem is written in Spanish, German, Bengali, Swahili and so on — a good test of language transfer.

What does it test?

MGSM measures performance in languages other than english. In detail, it checks:

  • same math quality across many languages
  • understanding of non-English problem statements
  • reasoning transfer from English to other languages
  • fair treatment of low-resource languages

How to read the score

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Frequently asked questions

What is the MGSM benchmark?

MGSM (Multilingual Grade School Math) is GSM8K translated into about ten other languages. It shows whether a model's math ability survives when the problem is written in Spanish, German, Bengali, Swahili and so on — a good test of language transfer.

What does MGSM test?

MGSM mainly measures performance in languages other than English. Concretely, it checks: same math quality across many languages; understanding of non-English problem statements; reasoning transfer from English to other languages.

How is the MGSM score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get MGSM data and scores?

The official source is https://arxiv.org/abs/2210.03057. The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/mgsm/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 5 models tested

Reset
# Model Score Date Source Verified
1 ERNIE-5.5-Flash-Base
Baidu
87%
— HF model card baidu/ERNIE-5.5-Flash-Base (EM, 8-shot CoT, Unified Pipeline) Unverified
2 Gemma 3 27B PT
Google DeepMind
74.3%
27.02.2025 Report Verified
3 Gemma 3 12B PT
Google DeepMind
64.3%
27.02.2025 Report Verified
4 Gemma 3 4B PT
Google DeepMind
34.7%
27.02.2025 Report Verified
5 Gemma 3 1B PT
Google DeepMind
2.04%
27.02.2025 Report Verified

What now?

You've seen how models score on MGSM — here's what to do next.