Perplexity has launched Model Council, a new multi-model AI research feature that allows users to run a single query across multiple frontier AI systems simultaneously and receive a synthesized answer showing where the models agree — and where they differ.

The feature sends queries to leading models such as Claude Opus 4.6, GPT-5.2, and Gemini 3.0, then uses a synthesizer model to analyze discrepancies and produce a consolidated response. Model Council is available now to Perplexity Max subscribers on web, with mobile access coming soon.

As frontier AI systems become more advanced and specialized, performance increasingly varies by task. A model that performs strongly in coding may be less effective in research or creative reasoning. For organizations using AI in professional workflows, that variability creates a practical challenge: determining which model is most reliable for a specific query. Model Council introduces structured cross-model validation within a single interface to reduce single-model dependency risk.

When users select Model Council inside the Perplexity interface, their query is sent simultaneously to three models available on the platform — examples include Claude Opus 4.6, GPT-5.2, and Gemini 3.0.

After each model generates its response, a synthesizer model evaluates the outputs. It:

The final response clearly shows where models converge and where they differ, offering transparency into reasoning variation.

Perplexity argues that every AI model has blind spots. Models may:

For low-stakes queries, those differences may not matter. But for research that informs financial decisions, strategic planning, or professional output, relying on a single model can introduce risk.

Model Council builds on Perplexity’s existing multi-model platform. Instead of requiring users to manually test prompts across different systems, the feature automates cross-model validation within a single workflow.

When models converge, users may move faster with greater confidence. When they diverge, users are prompted to investigate further.

Perplexity suggests Model Council is particularly useful for:

The feature is currently available to Perplexity Max subscribers, indicating a focus on advanced or professional users.

Q: What is Model Council?
A: It is a Perplexity feature that runs a single query across multiple AI models and synthesizes their responses into one structured answer showing agreement and disagreement.

Q: Which models are included?
A: Perplexity notes examples such as Claude Opus 4.6, GPT-5.2, and Gemini 3.0, though model availability may vary.

Q: Does this replace individual model selection?
A: No. Users can still select individual models. Model Council provides an additional research mode focused on multi-model comparison.

Q: Who can access it?
A: Model Council is available now for Perplexity Max subscribers on web, with mobile app support planned.

Perplexity’s Model Council emerges as organizations place greater emphasis on AI reliability and governance.

Who should care: Enterprise leaders, research teams, compliance officers, financial analysts, and professionals who rely on AI outputs for consequential decisions.

Why it matters now: As frontier AI systems become more specialized, performance divergence increases. A model optimized for coding may underperform in research. Another may produce fluent but incomplete reasoning. Enterprises deploying AI into strategic workflows must manage not just capability — but variability, bias exposure, and decision risk.

What decision this affects: How organizations structure AI oversight — whether a single model is treated as sufficient for research and decision workflows, or whether cross-model validation becomes a built-in safeguard.

Model Council formalizes a behavior advanced users already practice manually: checking answers across multiple systems before acting. By automating that comparison, Perplexity is introducing a lightweight governance layer directly into the product experience.

If multi-model validation becomes standard practice, the competitive question may no longer be “Which model is best?” — but “How do we verify before we trust?”

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing, image, and idea-generation support from ChatGPT, an AI assistant. However, the final perspective and editorial choices are solely Alicia Shapiro’s. Special thanks to ChatGPT for assistance with research and editorial support in crafting this article.