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reasoning benchmark

MBPP EvalPlus

MBPP (Mostly Basic Python Problems) is a benchmark of 974 crowd-sourced Python programming problems designed to be solvable by entry-level programmers. EvalPlus extends MBPP with significantly more test cases (35x) for more rigorous evaluation of LLM-synthesized code, providing high-quality and precise evaluation.

Updated Aug 11, 2026

Models2
Model coverage2
MetricScore
EvidenceB

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MBPP EvalPlus Ranking

Higher score ranks better on this benchmark.

2 rows
Columns

Show columns

01MELlama 3.1 405B InstructMeta88.6%100.0%2CAug 11, 2026
02MELlama 3.3 70B InstructMeta87.6%0.0%2CAug 11, 2026

MBPP EvalPlus Score Distribution

A closer view of the leading scores on this benchmark.

MBPP EvalPlus

MBPP EvalPlus Highlights

The leading models and scores on this benchmark.

Rank #1Llama 3.1 405B Instruct88.6%Rank #2Llama 3.3 70B Instruct87.6%

What is MBPP EvalPlus?

What MBPP EvalPlus measures and how its scores work.

MBPP (Mostly Basic Python Problems) is a benchmark of 974 crowd-sourced Python programming problems designed to be solvable by entry-level programmers. EvalPlus extends MBPP with significantly more test cases (35x) for more rigorous evaluation of LLM-synthesized code, providing high-quality and precise evaluation.

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

Family
MBPP EvalPlus
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
No
Evaluation key
mbpp-evalplus|llm-stats-current

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

FAQ

Common questions about MBPP EvalPlus.

Which model scores highest on MBPP EvalPlus?

Llama 3.1 405B Instruct is currently ranked first with 88.6%.

What does MBPP EvalPlus measure?

MBPP (Mostly Basic Python Problems) is a benchmark of 974 crowd-sourced Python programming problems designed to be solvable by entry-level programmers. EvalPlus extends MBPP with significantly more test cases (35x) for more rigorous evaluation of LLM-synthesized code, providing high-quality and precise evaluation.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

2 model results are currently shown.

Does this benchmark affect the overall score?

No. This benchmark is shown for reference but does not contribute to the overall score.