Agentic benchmark
MLE-Bench Lite leaderboard
Every model the catalog carries a published MLE-Bench Lite value for, ranked by that value.
A lightweight machine-learning competition benchmark that measures whether models can iteratively train, evaluate, and improve ML systems in low-resource settings.
MLE-Bench Lite ranking
3 models with a published MLE-Bench Lite value, ordered by that value, highest first. Models the source has not scored on this benchmark are not listed — they are unmeasured, not last.
| Rank | Model | Provider | Autonomous iterative ML optimization |
|---|---|---|---|
| 1 |  Atria Dawn Preview | Shanghai Artificial Intelligence Laboratory | 86.2 |
| 2 |  MiniMax M2.7 | MiniMax | 66.6 |
| 3 | Agents-A1-4B | InternScience | 22.7 |
Evidence key: Observed
Rows are ordered by the value MiniMax M2.7: Early Echoes of Self-Evolution published, highest first. The source does not state whether a higher value is the better result, so this page does not either: for a benchmark that measures a rate of failure, read the table from the bottom.