Model ensemble
Also known as: LLM ensemble, mixture of agents, multi-model deliberation, panel of models
In a model ensemble, you send one prompt to several models simultaneously rather than routing it to a single one. Each model produces its own answer. A judge model, or a synthesis step, then compares the responses and produces a final answer that draws on the consensus, resolves contradictions, and fills gaps none of the individual models covered. The underlying intuition is borrowed from ensemble methods in traditional machine learning: combining several imperfect models often beats any one alone because their errors and blind spots are different.
What changed in 2026 is that ensembling became accessible as a simple API call. OpenRouter Fusion productized the pattern so a builder can call openrouter/fusion the same way they'd call any single model, with the ensemble logic running server-side. Anthropic's own research on multi-agent workflows showed similar gains from running the same model twice on the same prompt and synthesizing the outputs, suggesting that some of the quality lift comes from the synthesis step itself rather than from model diversity.
The practical tradeoff: a model ensemble call costs roughly 4-5x more than a single-model call and adds latency because you're waiting for all panel completions before synthesis. That math is worth it for high-stakes tasks where being wrong is expensive and multiple perspectives add genuine signal, such as deep research, legal analysis, or architecture decisions. It is not worth it for fast autocomplete, simple classification, or any task where speed and cost dominate quality on the margin.