Multi-Model Consensus

If three independent attempts agree, the answer is more trustworthy.

The Pattern

Run the same task through multiple models (or the same model with different prompts/temperatures), then:

• Compare outputs for agreement
• Flag divergence for human review or deeper investigation
• Use consensus as a confidence signal

When to Use

• Factual questions where correctness matters
• Classification or categorization tasks
• Any task where “the model might be wrong” is a real concern
• Calibrating confidence before acting on output

Implementation Notes

• Models should be truly independent (different providers or architectures)
• Same-model different-temperature is weaker but still useful
• Consensus doesn’t guarantee correctness, but divergence is a strong signal of uncertainty

Cost Considerations

This pattern multiplies inference cost by the number of models. Use it when:

• The cost of being wrong exceeds the cost of extra inference
• You need confidence, not just output
• The task is a bottleneck worth investing in

Anti-patterns

• Using consensus to avoid thinking (“they all agreed, must be right”)
• Ignoring the dissenting model when two agree and one doesn’t
• Running consensus on tasks where models share the same blind spots

Related Patterns

• Adversarial Review: One model challenges another’s output