language benchmark
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.
Updated Aug 11, 2026
Models13
Model coverage13
MetricScore
EvidenceB
MultiPL-E Ranking
Higher score ranks better on this benchmark.
| 01 | AC | 87.9% | 100.0% | 13 | C | |
| 02 | AC | 87.8% | 91.7% | 13 | C | |
| 03 | AC | 86.1% | 83.3% | 13 | C | |
| 04 | MA | 85.7% | 75.0% | 13 | C | |
| 05 | MA | 85.7% | 66.7% | 13 | C | |
| 06 | AC | 75.4% | 58.3% | 13 | C | |
| 07 | AC | 75.1% | 50.0% | 13 | C | |
| 08 | AC | 72.8% | 41.7% | 13 | C | |
| 09 | AC | 70.4% | 33.3% | 13 | C | |
| 10 | AC | 69.2% | 25.0% | 13 | C | |
| 11 | AC | 65.9% | 16.7% | 13 | C | |
| 12 | AC | 65.8% | 8.3% | 13 | C | |
| 13 | AC | 59.1% | 0.0% | 13 | C |
MultiPL-E Score Distribution
A closer view of the leading scores on this benchmark.
MultiPL-E Highlights
The leading models and scores on this benchmark.
What is MultiPL-E?
What MultiPL-E measures and how its scores work.
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.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- MultiPL-E
- Modality
- text
- Primary category
- language
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- multipl-e|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about MultiPL-E.
Which model scores highest on MultiPL-E?
Qwen3-235B-A22B-Instruct-2507 is currently ranked first with 87.9%.
What does MultiPL-E measure?
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.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
13 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.