reasoning benchmark
MBPP EvalPlus (base) Leaderboard
MBPP EvalPlus (base) is a benchmark of 974 crowd-sourced Python programming problems for evaluating LLM-synthesized code with expanded test cases.
Updated Sep 5, 2026
Models1
Model coverage1
MetricScore
EvidenceB
MBPP EvalPlus (base) Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelME | Score72.80% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
MBPP EvalPlus (base) Highlights
The leading models and scores on this benchmark.
The Top AI Models for MBPP EvalPlus (base)
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis mbpp evalplus (base) AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01MEMeta
- Score
- 72.80%
- Speed
- Up to 2,047.00 tok/s via Cerebras
Strengths
- Ranks #1 of 1 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures MBPP EvalPlus (base), not total model capability
Selection summary
Best AI Models for MBPP EvalPlus (base)
Llama 3.1 8B Instruct currently leads MBPP EvalPlus (base) with 72.80%. 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 is MBPP EvalPlus (base)?
What MBPP EvalPlus (base) measures and how its scores work.
MBPP EvalPlus (base) is the EvalPlus extension of MBPP (Mostly Basic Python Problems), a benchmark of 974 Python programming problems designed to be solvable by entry-level programmers.
It measures Score, reported as a ratio, for LLM-synthesized code evaluated on the Python programming problems.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- MBPP EvalPlus (base)
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- overall
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about MBPP EvalPlus (base).
Which model scores highest on MBPP EvalPlus (base)?
Llama 3.1 8B Instruct is currently ranked first with 72.80%.
What are the top three models on MBPP EvalPlus (base)?
The current leaders are Llama 3.1 8B Instruct (72.80%).
Which MBPP EvalPlus (base) model has the lowest official input price?
No matched official input price is currently available.
Which models are fastest among MBPP EvalPlus (base) results?
The fastest matched records are Llama 3.1 8B Instruct (2,047.00 tok/s via Cerebras).
Does the highest score result prove overall model quality?
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.