MultiPL-E
MultiPL-E is a scalable and extensible system for translating unit test-driven code generation benchmarks to multiple programming languages. It extends HumanEval and MBPP Python benchmarks to 18 additional programming languages, enabling evaluation of neural code generation models across diverse programming paradigms and language features.
Qwen3-235B-A22B-Instruct-2507 from Alibaba Cloud / Qwen Team currently leads the MultiPL-E leaderboard with a score of 0.879 across 13 evaluated AI models.
What MultiPL-E measures
MultiPL-E is a text benchmark that evaluates large language models on general and language tasks. LLM Stats tracks 13 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 general and best AI for language leaderboards.
Publication
- Paper
- MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation
- Authors
- Federico Cassano, John Gouwar, Daniel Nguyen, Sydney Nguyen, and 9 others
- Published
- arXiv
- 2208.08227
Abstract
Large language models have demonstrated the ability to generate both natural language and programming language text. Such models open up the possibility of multi-language code generation: could code generation models generalize knowledge from one language to another? Although contemporary code generation models can generate semantically correct Python code, little is known about their abilities with other languages. We propose MultiPL-E, a system for translating unit test-driven code generation benchmarks to new languages. We create the first massively multilingual code generation benchmark by using MultiPL-E to translate two popular Python code generation benchmarks to 18 additional programming languages. We use MultiPL-E to extend the HumanEval benchmark and MBPP benchmark to 18 languages that encompass a range of programming paradigms and popularity. Using these new parallel benchmarks, we evaluate the multi-language performance of three state-of-the-art code generation models: Codex, CodeGen, and InCoder. We find that Codex matches or even exceeds its performance on Python for several other languages. The range of programming languages represented in MultiPL-E allow us to explore the impact of language frequency and language features on model performance. Finally, the MultiPL-E approach of compiling code generation benchmarks to new programming languages is both scalable and extensible, making it straightforward to evaluate new models, benchmarks, and languages.
Qwen3-235B-A22B-Instruct-2507 leads with 87.9%, followed by Qwen3-Next-80B-A3B-Instruct at 87.8% and Qwen3 VL 235B A22B Instruct at 86.1%.
Progress Over Time
Interactive timeline showing model performance evolution on MultiPL-E
MultiPL-E Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | 235B | — | — | ||
| 2 | Alibaba Cloud / Qwen Team | 80B | — | — | ||
| 3 | Alibaba Cloud / Qwen Team | 236B | 262K | $0.30 / $1.50 | ||
| 4 |  Moonshot AI | 1.0T | — | — | ||
| 4 | Moonshot AI | 1.0T | — | — | ||
| 6 | Alibaba Cloud / Qwen Team | 33B | — | — | ||
| 7 | Alibaba Cloud / Qwen Team | 73B | — | — | ||
| 8 | Alibaba Cloud / Qwen Team | 15B | — | — | ||
| 9 | Alibaba Cloud / Qwen Team | 8B | — | — | ||
| 10 | Alibaba Cloud / Qwen Team | 72B | — | — | ||
| 11 | Alibaba Cloud / Qwen Team | 235B | — | — | ||
| 12 | Alibaba Cloud / Qwen Team | 7B | — | — | ||
| 13 | Alibaba Cloud / Qwen Team | 8B | — | — |
FAQ
Common questions about MultiPL-E.
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