Multipl-E MBPP
MultiPL-E extends the Mostly Basic Python Problems (MBPP) benchmark to 18+ programming languages for evaluating multilingual code generation capabilities. MBPP contains 974 crowd-sourced programming problems designed to be solvable by entry-level programmers, covering programming fundamentals and standard library functionality. Each problem includes a task description, code solution, and automated test cases.
Llama 3.1 405B Instruct from Meta currently leads the Multipl-E MBPP leaderboard with a score of 0.657 across 3 evaluated AI models.
What Multipl-E MBPP measures
Multipl-E MBPP is a text benchmark that evaluates large language models on reasoning and general tasks. LLM Stats tracks 3 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.6, with the leader reaching 0.7.
Compare leaders on the best AI for reasoning and best AI for general 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.
Llama 3.1 405B Instruct leads with 65.7%, followed by Llama 3.1 70B Instruct at 62.0% and Llama 3.1 8B Instruct at 52.4%.
Progress Over Time
Interactive timeline showing model performance evolution on Multipl-E MBPP
Multipl-E MBPP Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | 405B | — | — | |||
| 2 | 70B | — | — | |||
| 3 | 8B | — | — |
FAQ
Common questions about Multipl-E MBPP.
More evaluations to explore
Related benchmarks in the same category
A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.
A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.
All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.
Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.
Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions