OpenBenchML
Benchmark, Compare, and Rank Machine Learning Models
Platform Features
Everything you need to evaluate, compare, and rank machine learning models objectively.
Upload Models
Upload your trained ML models (.pkl, .joblib, .onnx, .pt)
Benchmark Datasets
Run against Iris, Wine, MNIST, and more standard datasets
Performance Metrics
Measure accuracy, latency, memory, CPU, and model size
Docker Sandbox
Secure model execution in isolated Docker containers
Public Leaderboard
Compare your models against the community
REST API
Full API access for programmatic benchmarking
Leaderboard Preview
Top performing models ranked by composite score
| Rank | Model | Owner | Dataset | Accuracy | Latency | Score |
|---|---|---|---|---|---|---|
| 1 | XGBoost v2.1 | ml_champion | Iris | 98.7% | 1.2 ms | 96.4 |
| 2 | RandomForest Opt | data_science_pro | Wine | 97.2% | 0.8 ms | 94.8 |
| 3 | NeuralNet-Lite | ai_researcher | MNIST | 96.5% | 3.4 ms | 93.1 |
1,247
Models Benchmarked
12
Datasets Available
8,439
Benchmarks Run