AI search startup Perplexity has unveiled a new feature called Model Councils, aimed at improving the quality, balance, and reliability of AI-generated answers by aggregating responses from multiple leading artificial intelligence models. The launch reflects a growing industry push toward transparency and multi-model collaboration as AI tools become more deeply embedded in everyday decision-making.
Model Councils allows users to receive a single synthesized response generated from several AI systems rather than relying on one underlying model. Instead of presenting one perspective, the feature compares, evaluates, and blends outputs from different large language models, offering users a more nuanced and comprehensive answer. Perplexity says the goal is to reduce bias, improve factual accuracy, and surface competing viewpoints where appropriate.
The move comes amid increasing scrutiny of AI-generated content, particularly concerns around hallucinations, overconfidence, and opaque decision-making. As AI tools are increasingly used for research, education, business analysis, and news consumption, companies are under pressure to demonstrate that their systems can deliver trustworthy and well-sourced information.
Perplexity has positioned itself as an “answer engine” rather than a traditional chatbot, emphasizing citations, sourcing, and real-time web access. Model Councils builds on that positioning by acknowledging that no single AI model is consistently superior across all domains. Different models excel at different tasks—some at reasoning, others at creativity, and others at summarization or technical accuracy.
By combining multiple models, Perplexity aims to create what it describes as a “committee-style” response process. This mirrors how human experts often work—by consulting multiple sources before arriving at a conclusion. The company argues that this approach can help users better understand uncertainty, trade-offs, and areas of disagreement, rather than presenting AI output as a definitive answer.
The launch also reflects broader trends in the AI ecosystem. Enterprises are increasingly adopting multi-model strategies to avoid dependence on a single provider and to optimize performance across use cases. At the same time, developers are seeking ways to make AI systems more interpretable and accountable, especially as regulators begin to examine how automated decision-making tools are deployed.
Industry analysts note that Model Councils could appeal particularly to professionals and researchers who value breadth of perspective over speed alone. By surfacing how different models respond to the same query, users gain insight into the strengths and limitations of each system—something that is often hidden in consumer-facing AI products.
However, aggregating multiple AI models also introduces new challenges. Combining outputs requires careful weighting and reconciliation to avoid amplifying errors or contradictions. There are also cost and performance considerations, as running multiple models simultaneously can be more resource-intensive than relying on a single system. Perplexity has not disclosed which models are included in Model Councils or how the aggregation process is weighted, citing competitive reasons.
The feature arrives at a time when AI companies are experimenting with new interfaces and governance mechanisms. Rather than positioning AI as an all-knowing oracle, tools like Model Councils suggest a shift toward AI as an assistant that supports human judgment. This framing may prove critical as users become more discerning about when—and how—to trust machine-generated information.
For Perplexity, the launch is also a strategic differentiator in an increasingly crowded market. With major technology companies integrating AI into search engines, productivity software, and operating systems, smaller players must distinguish themselves through design choices and philosophy. Emphasizing plurality and synthesis over single-model dominance may help Perplexity carve out a distinct identity.
As AI adoption accelerates, features like Model Councils highlight an important evolution in how intelligence is delivered—not as a single voice, but as a structured conversation. Whether this approach becomes standard across the industry remains to be seen, but it signals a growing recognition that better answers often come from multiple perspectives, not just faster algorithms.
