Model Ensembling

Benched.ai Editorial Team

Model ensembling combines outputs from multiple models to improve accuracy, robustness, or safety.

Ensemble Strategies

Strategy	Combination Rule	Example
Majority voting	Pick answer with most votes	Multi-choice QA
Confidence-weighted	Softmax of scores	Search reranking
Cascade	Fallback to bigger model	Cost reduction
Mixture-of-Experts	Router picks sub-model	Long-context

Trade-offs

Higher quality but increased latency & cost.
Hard voting may discard nuanced answers; weighted merges retain confidence.

Current Trends (2025)

GPU inference kernels batch two models in same launch to save latency.
Safety ensembles veto toxic generations before output.
Adaptive ensembles switch members based on user locale.

Implementation Tips

Normalize confidence scores across models for fair weighting.
Log which model wins per request to guide future pruning.
Run A/B tests to verify ensemble gain over best single model.