llmboard.aiAI model intelligence
Home

Model Rankings

OverallOpen ModelsAgentCodingReasoningMathKnowledgeInstruction FollowingTextVision
Image GenerationImage Editing
Video GenerationImage to VideoVideo Editing
Text to SpeechSpeech to Text
Embeddings

Efficiency

Chat Token PricingImage PricingVideo PricingAudio Pricing
Chat Speed & LatencyProvider Reliability

Benchmarks

GPQAMMLU-ProAIME 2025SWE-Bench VerifiedMMLUHumanity's Last ExamLiveCodeBenchMATHHumanEvalMMMU-Pro
All Benchmarks

Tools

Model DirectoryCompare Models

Scoring & Data

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 Leaderboard

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 17, 2026

Models3
Model coverage3
MetricScore
EvidenceB

On this page

  • Ranking
  • Highlights
  • Distribution
  • Top models
  • About
  • FAQ

Multipl-E MBPP Ranking

Higher score ranks better on this benchmark.

3 rows
Columns

Show columns

Sort by
Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelMELlama 3.1 405B InstructMetaScore65.7%Percentile100.0%Participants3EvidenceCEvaluatedAug 17, 2026
Rank02ModelMELlama 3.1 70B InstructMetaScore62.0%Percentile50.0%Participants3EvidenceCEvaluatedAug 17, 2026
Rank03ModelMELlama 3.1 8B InstructMetaScore52.4%Percentile0.0%Participants3EvidenceCEvaluatedAug 17, 2026

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%

Multipl-E MBPP Score Distribution

A closer view of the leading scores on this benchmark.

Multipl-E MBPP

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.

Ranking basisThis multipl-e mbpp AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.

  1. 01
    ME
    Llama 3.1 405B InstructMeta
    Score
    65.7%
    Speed
    Up to 100 tok/s via Bedrock

    Strengths

    • Ranks #1 of 3 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Multipl-E MBPP, not total model capability
  2. 02
    ME
    Llama 3.1 70B InstructMeta
    Score
    62.0%
    Speed
    Up to 1,204 tok/s via Cerebras

    Strengths

    • Ranks #2 of 3 compared models
    • 50th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Multipl-E MBPP, not total model capability
  3. 03
    ME
    Llama 3.1 8B InstructMeta
    Score
    52.4%
    Speed
    Up to 2,047 tok/s via Cerebras

    Strengths

    • Ranks #3 of 3 compared models
    • 0th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Multipl-E MBPP, not total model capability

Selection summary

Best AI Models for Multipl-E MBPP

Llama 3.1 405B Instruct currently leads Multipl-E MBPP with 65.7%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.

Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.

Benchmark rank #1Llama 3.1 405B Instruct65.7% · Up to 100 tok/s via BedrockBenchmark rank #2Llama 3.1 70B Instruct62.0% · Up to 1,204 tok/s via CerebrasBenchmark rank #3Llama 3.1 8B Instruct52.4% · Up to 2,047 tok/s via Cerebras

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.