language benchmark
Multipl-E HumanEval
MultiPL-E is a scalable and extensible approach to benchmarking neural code generation that translates unit test-driven code generation benchmarks across multiple programming languages. It extends the HumanEval benchmark to 18 additional programming languages, enabling evaluation of code generation models across diverse programming paradigms and providing insights into how models generalize programming knowledge across language boundaries.
Updated Aug 7, 2026
Published models3
Registry coverage3
MetricScore
EvidenceB
Multipl-E HumanEval leaderboard
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | ME | 75.2% | 100.0% | 3 | C | |
| 2 | ME | 65.5% | 50.0% | 3 | C | |
| 3 | ME | 50.8% | 0.0% | 3 | C |
Score distribution
Top published rows on the benchmark's original scale.
Multipl-E HumanEval highlights
The top published results on this benchmark's own scale.
What is Multipl-E HumanEval?
Definition and scoring fields from the benchmark registry.
MultiPL-E is a scalable and extensible approach to benchmarking neural code generation that translates unit test-driven code generation benchmarks across multiple programming languages. It extends the HumanEval benchmark to 18 additional programming languages, enabling evaluation of code generation models across diverse programming paradigms and providing insights into how models generalize programming knowledge across language boundaries.
Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.
- Family
- Multipl-E HumanEval
- Modality
- text
- Primary category
- language
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- multipl-e-humaneval|llm-stats-current
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
FAQ
Common questions about Multipl-E HumanEval.
Which model scores highest on Multipl-E HumanEval?
Llama 3.1 405B Instruct is currently ranked first with 75.2%.
What does Multipl-E HumanEval measure?
MultiPL-E is a scalable and extensible approach to benchmarking neural code generation that translates unit test-driven code generation benchmarks across multiple programming languages. It extends the HumanEval benchmark to 18 additional programming languages, enabling evaluation of code generation models across diverse programming paradigms and providing insights into how models generalize programming knowledge across language boundaries.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
3 unique published model results are currently shown.
Does this benchmark affect the overall score?
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.