Perplexity has introduced Model Council, a multi-model research capability designed to address variability in large language model performance by executing a single query against multiple AI systems.
The feature sends queries concurrently to three models within the Perplexity interface, examples include GPT-5.2, Claude Opus 4.6, and Gemini 3, then uses an internal synthesizer model to evaluate each response, identify consensus, flag differences, and deliver a unified answer with insights on divergence and agreement.
Model Council is positioned to reduce reliance on a single model’s output, a practical concern for enterprise workflows where task-specific strengths vary across models and unchecked hallucinations or blind spots can affect decision quality.
By surfacing structured comparisons, the system aims to provide greater transparency into reasoning differences and support greater confidence in results used for research, verification, strategic planning, or complex analysis.
Initially available on the web to Perplexity Max subscribers, Model Council reflects a broader trend toward multi-model orchestration as part of deployed AI platforms, enabling users to balance performance, reliability, and risk across diverse AI engines without manual switching or parallel evaluation workflows.