Perplexity Launches Model Council — Parallel AI Models Reduce Hallucinations

Model Council Launch

Perplexity launched Model Council, a revolutionary system that runs multiple frontier AI models in parallel including Claude, GPT-5.2, and Gemini to generate unified, cross-validated answers.

This approach overcomes the limitations of single models and combines the strengths of multiple models to provide more reliable results.

How It Works

The core mechanism of Model Council:

Parallel execution: User queries are sent simultaneously to multiple frontier models

Independent reasoning: Each model generates answers independently

Cross-validation: Answers from models are compared for agreement

Unified answer: Final answer generated based on consensus information

This process allows even if one model generates incorrect information (hallucinates), other models can detect and correct it.

Performance Improvements

Perplexity states Model Council delivers these improvements:

Reasoning quality: Significantly improved through collective intelligence of multiple models

Reduced hallucinations: Cross-validation minimizes incorrect information generation

Reliability: Answers agreed upon by multiple models are more trustworthy

The system's advantages are particularly pronounced for complex questions or situations where fact-checking is critical.

Cost and Performance Tradeoffs

Model Council is innovative but has clear tradeoffs:

Advantages:

Higher accuracy and reliability

Reduced hallucination errors

Combined strengths of multiple models

Disadvantages:

Increased computing costs from running multiple models simultaneously

Potentially longer response times

Increased operational complexity

Perplexity appears to have determined these additional costs are justified by the value provided to users.

AI Industry Trend

Model Council reflects an important AI industry trend:

Moving beyond single model dependence: Rather than finding one "best" model, achieving better results through collaboration of multiple models.

Ensemble approach: Applying ensemble techniques long used in machine learning to LLMs. Combining predictions from multiple models generally yields better performance than a single model.

Reliability first: Movement toward prioritizing accuracy and reliability over speed or cost.

Competitor Response

Other AI companies are likely experimenting with similar approaches:

OpenAI: Already offers multiple models like GPT-4o and o3-mini, likely using ensemble techniques internally

Anthropic: Utilizes consensus mechanisms between models in Constitutional AI

Google: Potential for multi-model validation in Gemini series

Perplexity's public launch of this as a feature appears to be part of a differentiation strategy.

User Experience

From a user perspective, Model Council provides:

More trustworthy answers

Particularly useful in fields where fact-checking is important: research, healthcare, legal, etc.

Quality improvement worth accepting slightly slower response times

Perplexity is a company with strengths in Retrieval-Augmented Generation (RAG), and Model Council further strengthens these advantages.

Sources

LLM Stats: AI Updates Today (February 2026)