reasoning benchmark
MBPP pass@1 is a benchmark of 974 crowd-sourced Python programming problems designed to be solvable by entry-level programmers.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score70.00% | 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 pass@1 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
Ministral 8B Instruct currently leads MBPP pass@1 with 70.00%. 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 pass@1 measures and how its scores work.
MBPP (Mostly Basic Python Problems) consists of Python programming problems, each with a task description, code solution, and 3 automated test cases.
It measures the percentage of problems solved correctly on the first attempt using the pass@1 evaluation metric.
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 pass@1.
Ministral 8B Instruct is currently ranked first with 70.00%.
The current leaders are Ministral 8B Instruct (70.00%).
No matched official input price is currently available.
The fastest matched records are Ministral 8B Instruct (0.10 tok/s via Mistral AI).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures the percentage of problems solved correctly on the first attempt using the pass@1 evaluation metric.
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.