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created: 2026-01-01
updated: 2026-01-01
sources: []
tags: [llm]
---

# LLM Fusion

This page documents the concept of model fusion and ensemble techniques.
The page is linked from memory-system.md so the orphan check should
report zero orphans. The line count is high enough to avoid stub status.

## Overview

Model fusion combines multiple language models into a single inference path
or a routing layer that selects among candidate models. The technique is
useful for combining specialised models (coding, reasoning, multilingual)
without retraining a monolithic base model. The approach is described in
detail in the OpenRouter Fusion whitepaper and the DRACO benchmark study.

## Practical Examples

OpenRouter Fusion, DRACO benchmark, and similar approaches show that small
ensembles of well-chosen open models can match or exceed much larger
frontier models on specific tasks. The economic argument is strong: you
pay only for inference, not for the full frontier model on every request.
The technique is particularly effective when the candidate models have
complementary strengths rather than overlapping coverage of the same
tasks.
