reasoning benchmark
MBPP Plus is an enhanced benchmark of 974 crowd-sourced Python programming problems designed to be solvable by entry-level programmers, with additional test cases for evaluation.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score78.33% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis mbpp plus AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Mistral Small 3.2 24B Instruct currently leads MBPP Plus with 78.33%. 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.
What MBPP Plus measures and how its scores work.
MBPP Plus is a benchmark containing Python programming tasks, code solutions, and automated test cases covering programming fundamentals and standard library functionality.
It measures Score, reported as a ratio, on these Python programming problems.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about MBPP Plus.
Mistral Small 3.2 24B Instruct is currently ranked first with 78.33%.
The current leaders are Mistral Small 3.2 24B Instruct (78.33%).
No matched official input price is currently available.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures Score, reported as a ratio, on these Python programming problems.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.