MLE-Bench Lite leaderboard
1 ranked models · higher is better
View accessible chart data
| Model | Rank | Provider | Score |
|---|---|---|---|
| MiniMax M2.7 | #1 | MiniMax | 66.6% |
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A lightweight machine-learning competition benchmark that measures whether models can iteratively train, evaluate, and improve ML systems in low-resource settings.
Data verified 21 Jul 2026 · Methodology 1.6.0
Visual analysis
Switch between model placement, score distribution and descriptive provider averages. Every view uses the same sourced leaderboard.
1 ranked models · higher is better
| Model | Rank | Provider | Score |
|---|---|---|---|
| MiniMax M2.7 | #1 | MiniMax | 66.6% |
One best score per model · higher is better
| Rank | Model | Provider | License | Evidence use | Score |
|---|---|---|---|---|---|
| #1 | MiniMax M2.7 minimax-m2-7 | MiniMax | open | Reference only | 66.6% |
About MLE-Bench Lite
A lightweight machine-learning competition benchmark that measures whether models can iteratively train, evaluate, and improve ML systems in low-resource settings. Results stay tied to the exact model variant and evaluation system. Multiple systems for the same model use the best published score on this page; overall Lumina scoring uses the median of ranking-eligible rows.
Open benchmark source ↗FAQ
A lightweight machine-learning competition benchmark that measures whether models can iteratively train, evaluate, and improve ML systems in low-resource settings.
MiniMax M2.7 by MiniMax currently leads with 66.6%.
1 model in the LuminaBench cohort have a qualifying score on this benchmark.
No. This benchmark is display-only and does not enter the overall Lumina composite.
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