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.
Llama 3.1 405B Instruct from Meta currently leads the Multipl-E HumanEval leaderboard with a score of 0.752 across 3 evaluated AI models.
Llama 3.1 405B Instruct leads with 75.2%, followed by Llama 3.1 70B Instruct at 65.5% and Llama 3.1 8B Instruct at 50.8%.
Progress Over Time
Interactive timeline showing model performance evolution on Multipl-E HumanEval
Multipl-E HumanEval Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | 405B | — | — | |||
| 2 | 70B | — | — | |||
| 3 | 8B | — | — |
FAQ
Common questions about Multipl-E HumanEval.
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