Model Ensemble Router

Model Ensemble Router

Combine several open-source models to get better answers at lower cost than one frontier model.

When to use

• You want frontier-ish quality without paying frontier prices.
• You have access to multiple local/open models and want to blend or select their outputs.

Procedure

1. Identify 2-4 open models with complementary strengths (e.g. one strong at reasoning, one fast, one cheap).
2. Decide strategy: (a) run all and pick best via a judge model, (b) cascade cheap->expensive until confidence, or (c) ensemble-average structured outputs.
3. For selection: run a lightweight judge prompt that scores each candidate on correctness/quality.
4. For structured tasks, compare outputs and take the majority or the judge's pick.
5. Log cost + quality per strategy and tune.

Pitfalls

• Running every model on every request can cost more than one good model. Use cascades or judges, not raw fan-out.
• A weak judge can pick the wrong answer. Validate the judge against known-good labels first.

Verification

• Show a side-by-side of the ensemble vs a single frontier model on the same prompts, with cost.