reasoning benchmark
Multipl-E MBPP Leaderboard
Multipl-E MBPP extends the Mostly Basic Python Problems (MBPP) benchmark to 18+ programming languages with 974 crowd-sourced programming problems.
Updated Sep 5, 2026
Models3
Model coverage3
MetricScore
EvidenceB
Multipl-E MBPP Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelME | Score65.70% | Percentile100.00% | Participants3 | EvidenceC | Evaluated |
| Rank02 | ModelME | Score62.00% | Percentile50.00% | Participants3 | EvidenceC | Evaluated |
| Rank03 | ModelME | Score52.40% | Percentile0.00% | Participants3 | EvidenceC | Evaluated |
Multipl-E MBPP Highlights
The leading models and scores on this benchmark.
Multipl-E MBPP Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for Multipl-E MBPP
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
What is Multipl-E MBPP?
What Multipl-E MBPP measures and how its scores work.
Multipl-E MBPP is a benchmark for evaluating multilingual code generation capabilities using programming problems designed to be solvable by entry-level programmers, covering programming fundamentals and standard library functionality.
It measures performance on multilingual code generation using the Score metric, reported as a ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Multipl-E MBPP
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- overall
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Multipl-E MBPP.
Which model scores highest on Multipl-E MBPP?
Llama 3.1 405B Instruct is currently ranked first with 65.70%.
What are the top three models on Multipl-E MBPP?
The current leaders are Llama 3.1 405B Instruct (65.70%), Llama 3.1 70B Instruct (62.00%), and Llama 3.1 8B Instruct (52.40%).
Which Multipl-E MBPP model has the lowest official input price?
No matched official input price is currently available.
Which models are fastest among Multipl-E MBPP results?
The fastest matched records are Llama 3.1 8B Instruct (2,047.00 tok/s via Cerebras), Llama 3.1 70B Instruct (1,204.00 tok/s via Cerebras), and Llama 3.1 405B Instruct (42.00 tok/s via Google).
Does the highest score result prove overall model quality?
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.