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language benchmark

Multipl-E HumanEval Leaderboard

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.

Updated Aug 17, 2026

Models3
Model coverage3
MetricScore
EvidenceB

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Multipl-E HumanEval Ranking

Higher score ranks better on this benchmark.

3 rows
Columns

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Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelMELlama 3.1 405B InstructMetaScore75.2%Percentile100.0%Participants3EvidenceCEvaluatedAug 17, 2026
Rank02ModelMELlama 3.1 70B InstructMetaScore65.5%Percentile50.0%Participants3EvidenceCEvaluatedAug 17, 2026
Rank03ModelMELlama 3.1 8B InstructMetaScore50.8%Percentile0.0%Participants3EvidenceCEvaluatedAug 17, 2026

Multipl-E HumanEval Highlights

The leading models and scores on this benchmark.

Rank #1Llama 3.1 405B Instruct75.2%Rank #2Llama 3.1 70B Instruct65.5%Rank #3Llama 3.1 8B Instruct50.8%

Multipl-E HumanEval Score Distribution

A closer view of the leading scores on this benchmark.

Multipl-E HumanEval

The Top AI Models for Multipl-E HumanEval

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 humaneval 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
    75.2%
    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 HumanEval, not total model capability
  2. 02
    ME
    Llama 3.1 70B InstructMeta
    Score
    65.5%
    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 HumanEval, not total model capability
  3. 03
    ME
    Llama 3.1 8B InstructMeta
    Score
    50.8%
    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 HumanEval, not total model capability

Selection summary

Best AI Models for Multipl-E HumanEval

Llama 3.1 405B Instruct currently leads Multipl-E HumanEval with 75.2%. 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 Instruct75.2% · Up to 100 tok/s via BedrockBenchmark rank #2Llama 3.1 70B Instruct65.5% · Up to 1,204 tok/s via CerebrasBenchmark rank #3Llama 3.1 8B Instruct50.8% · Up to 2,047 tok/s via Cerebras

What is Multipl-E HumanEval?

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

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.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
Multipl-E HumanEval
Modality
text
Primary category
language
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
multipl-e-humaneval|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about Multipl-E HumanEval.

Which model scores highest on Multipl-E HumanEval?

Llama 3.1 405B Instruct is currently ranked first with 75.2%.

What does Multipl-E HumanEval measure?

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.

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.