llmboard.aiLeaderboard Center
Overall
Overall RankingOpen Models
Tools
Model DirectoryCompare Models
Capabilities
CodingReasoningMathKnowledgeInstruction Following
Price & Efficiency
Price & ValueCapability vs. PriceRuntime Performance
Modalities
Image GenerationVideo GenerationSpeech ModelsEmbeddings
Core Benchmarks
GPQAMMLU-ProAIME 2025SWE-Bench VerifiedMMLUHumanity's Last ExamLiveCodeBenchMATHHumanEvalMMMU-ProView all benchmarks
Methods
Scoring & Data
393 models668 benchmarks

Leaderboard Center

Overall RankingCodingCore BenchmarksPrice & ValueRuntime Performance

Modalities

All ModelsImage GenerationImage EditingVideo GenerationImage-to-VideoVideo EditingText-to-SpeechSpeech-to-TextEmbeddings

Data & Methods

Scoring MethodAll BenchmarksReasoningMath

Vendors

All VendorsOpenAIAnthropicGoogle
llmboard.aiCopyright 2026 llmboard.ai

reasoning benchmark

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.

Updated Aug 11, 2026

Models3
Model coverage3
MetricScore
EvidenceB

On this page

  • Ranking
  • Distribution
  • Highlights
  • About
  • FAQ

Multipl-E MBPP Ranking

Higher score ranks better on this benchmark.

3 rows
Columns

Show columns

01MELlama 3.1 405B InstructMeta65.7%100.0%3CAug 11, 2026
02MELlama 3.1 70B InstructMeta62.0%50.0%3CAug 11, 2026
03MELlama 3.1 8B InstructMeta52.4%0.0%3CAug 11, 2026

Multipl-E MBPP Score Distribution

A closer view of the leading scores on this benchmark.

Multipl-E MBPP

Multipl-E MBPP Highlights

The leading models and scores on this benchmark.

Rank #1Llama 3.1 405B Instruct65.7%Rank #2Llama 3.1 70B Instruct62.0%Rank #3Llama 3.1 8B Instruct52.4%

What is Multipl-E MBPP?

What Multipl-E MBPP measures and how its scores work.

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.

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
Yes
Evaluation key
multipl-e-mbpp|llm-stats-current

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 65.7%.

What does Multipl-E MBPP measure?

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.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

3 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.