MGSM
MGSM (Multilingual Grade School Math) is a benchmark of grade-school math problems. Contains 250 grade-school math problems manually translated from the GSM8K dataset into ten typologically diverse languages: Spanish, French, German, Russian, Chinese, Japanese, Thai, Swahili, Bengali, and Telugu. Evaluates multilingual mathematical reasoning capabilities.
Llama 4 Maverick from Meta currently leads the MGSM leaderboard with a score of 0.923 across 31 evaluated AI models.
What MGSM measures
MGSM is a text benchmark that evaluates large language models on math and reasoning tasks. LLM Stats tracks 31 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.8, with the leader reaching 0.9.
Compare leaders on the best AI for math and best AI for reasoning leaderboards.
Publication
- Paper
- Language Models are Multilingual Chain-of-Thought Reasoners
- Authors
- Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, and 8 others
- Published
- arXiv
- 2210.03057
Abstract
We evaluate the reasoning abilities of large language models in multilingual settings. We introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating 250 grade-school math problems from the GSM8K dataset (Cobbe et al., 2021) into ten typologically diverse languages. We find that the ability to solve MGSM problems via chain-of-thought prompting emerges with increasing model scale, and that models have strikingly strong multilingual reasoning abilities, even in underrepresented languages such as Bengali and Swahili. Finally, we show that the multilingual reasoning abilities of language models extend to other tasks such as commonsense reasoning and word-in-context semantic judgment. The MGSM benchmark is publicly available at https://github.com/google-research/url-nlp.
Llama 4 Maverick leads with 92.3%, followed by 
o3-mini at 92.0% and 
Claude 3.5 Sonnet at 91.6%.
Progress Over Time
Interactive timeline showing model performance evolution on MGSM
MGSM Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Meta | 400B | — | — | ||
| 2 | OpenAI | — | — | — | ||
| 3 | Anthropic | — | — | — | ||
| 3 | Anthropic | — | — | — | ||
| 5 | 70B | — | — | |||
| 6 | OpenAI | — | — | — | ||
| 7 | Anthropic | — | — | — | ||
| 8 | Meta | 109B | — | — | ||
| 9 | OpenAI | — | 128K | $2.50 / $10.00 | ||
| 10 | OpenAI | — | — | — | ||
| 11 | OpenAI | — | 128K | $10.00 / $30.00 | ||
| 12 |  Google | — | — | — | ||
| 13 | OpenAI | — | — | — | ||
| 14 | 90B | — | — | |||
| 15 | Anthropic | — | — | — | ||
| 16 |  Alibaba Cloud / Qwen Team | 235B | — | — | ||
| 17 | Anthropic | — | — | — | ||
| 18 | Google | — | — | — | ||
| 19 |  Microsoft | 15B | — | — | ||
| 20 | Anthropic | — | — | — | ||
| 21 | OpenAI | — | — | — | ||
| 22 | 11B | — | — | |||
| 23 | Google | 8B | — | — | ||
| 24 | Microsoft | 4B | — | — | ||
| 25 | 2B | — | — | |||
| 26 | Microsoft | 60B | — | — | ||
| 27 | 3B | — | — | |||
| 28 | OpenAI | — | 16K | $0.50 / $1.50 | ||
| 29 | Google | 8B | — | — | ||
| 29 | 2B | — | — | |||
| 31 | Microsoft | 4B | — | — |
FAQ
Common questions about MGSM.
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