Key Highlights:

• The move strengthens Perplexity’s position in AI-powered search and research.
• Perplexity launches Model Council, a multi-model AI research feature.
• The tool runs one query across multiple frontier AI models simultaneously.
• It highlights agreement and disagreement between models in one response.

Perplexity AI has launched a new feature called Model Council, aimed at improving how users get accurate answers from artificial intelligence. The update allows a single query to run across multiple AI models at once, instead of relying on just one system.

The move matters as AI models become more specialized and inconsistent across tasks. What works well for coding may fail in research or creative work. Model Council is designed to surface those differences clearly.

What is Model Council and how does it work?

Model Council is a multi-model research mode built directly into the Perplexity interface. When users select it, the same query runs across three AI models at the same time.

A separate synthesizer model then reviews all responses. It resolves conflicts where possible and produces a single output. That answer clearly shows where models agree and where they diverge.

This removes the need for users to manually switch between models to verify information.

Why Perplexity is pushing a multi-model approach

AI performance varies widely depending on context. Some models miss nuance. Others lean toward specific viewpoints. Many fill information gaps with confident assumptions.

For users making decisions based on AI output, these blind spots can be risky. Model Council addresses this by exposing variation instead of hiding it.

When multiple models converge on the same answer, confidence increases. When they disagree, users know more verification is needed.

Where Model Council is most useful

Perplexity says Model Council is best suited for situations where accuracy and perspective matter most.

This includes investment research, where bias can affect financial outcomes. It also applies to complex decisions such as career planning or major purchases.

The feature also supports creative brainstorming by combining different reasoning styles. It can help with verification when users need to cross-check facts quickly.

Why this matters for AI-powered search

Perplexity has positioned itself around choice and transparency in AI responses. Model Council builds on that strategy by making model comparison part of the search process itself.

Instead of asking which AI model is best, users can now see how models think differently on the same question. That shift could shape how answer engines present information going forward.

As AI adoption grows, Perplexity is betting that showing uncertainty and disagreement is more useful than hiding it.