# Model Ensemble Router

> Use when you want better answers by combining multiple open-source models and picking the best result at lower cost.

- Skill: `harishkotra/model-ensemble-router` (Agent Skill)
- Install (CLI): `npx skillmds@latest add harishkotra/model-ensemble-router`
- Raw SKILL.md: https://api.skillmd.com/api/skills/harishkotra/model-ensemble-router/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: harishkotra (https://skillmd.com/u/harishkotra)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/harishkotra/model-ensemble-router

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# 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.