PERPLEXITY / MODEL COUNCIL
Asking one question to several AIs at once.
Perplexity's "Model Council" has expanded to its cloud platform, Perplexity Computer. Users can now pick 2 to 8 models — from OpenAI, Anthropic, and Google's frontier labs to open-weight models like GLM and Kimi — to run in parallel, surfacing where they agree and where they diverge.
The quiet history of "ask several AIs at once"
A feature that launched quietly, then expanded over five months.
Model Council, which Perplexity began rolling out in 2026, lets users fire one question at multiple AI models simultaneously and see the answers laid side by side. According to Perplexity's own blog post, the feature originally ran three different models per query; after each model generated an independent answer, Perplexity's built-in synthesizer analyzed the outputs and organized them into a structured comparison table. As Storyboard18 reported, the design rests on the premise that different models excel at different tasks — summarization, reasoning, creativity, math, coding, handling of sources, and so on. What made it distinctive wasn't just listing several answers, but surfacing exactly where they disagreed.
It's easy to confuse this with the "19-model parallel execution" feature that Perplexity Computer had already introduced on May 2, 2026. That feature can run up to 19 models at once, but it simply lays out raw outputs without a synthesized view highlighting agreement or disagreement. Model Council has always been a separate, comparison-and-synthesis-focused feature.
On July 28, 2026, The Register reported that Model Council, five months after its launch, had expanded to the cloud-based Perplexity Computer platform as well. According to the article, users can now freely choose between 2 and 8 models, with options spanning frontier-lab models from OpenAI, Anthropic, and Google as well as open-weight models like GLM and Kimi. This marks a clear pivot: from a single vendor handing down "the one correct answer," to a mechanism for cross-checking a group of models shaped by different philosophies and training data.
The expansion, in numbers
Five months after launch, the feature quietly grew.
Four steps to use it
Enter your question
Type the question you want compared, just like a normal search.
Pick 2 to 8 models
Options include frontier models from OpenAI, Anthropic, and Google, as well as open-weight models like GLM and Kimi.
Each model answers independently
Each selected model reasons and generates its answer on its own, without seeing the others' responses.
The synthesizer builds a unified view
The built-in synthesis engine analyzes all the answers and organizes agreements and disagreements into a structured comparison table. You can still open any individual model's full answer.
Who it helps, and how
The more you tend to take one AI's answer at face value, the more this gives you to work with.
Engineers: measure each model's strengths and blind spots
In coding and technical reasoning, different models tend to be strong in different languages and weak with different frameworks. Send the same implementation question to several models, and the comparison view instantly shows which one's suggestion is more likely to hide a bug or a stale API call. It works as a lightweight cross-review before you commit to any single model's advice.
Business: a second opinion before you decide
For questions without a single right answer — market research, competitive analysis — the way models disagree is itself information. A conclusion every model reaches is more trustworthy; a point where they split marks where more digging is needed. It gives internal proposals and approvals a new metric: cross-model consensus.
PMs: more sparring partners for specs and priorities
Questions about requirements or prioritization often get different framings depending on which AI you ask. Send the same product problem to 2 to 8 models, and the synthesizer's agreement/disagreement summary can surface a point your team overlooked — or a judgment call that was quietly shaped by one model's particular habits.
A single AI vs. Model Council
| Before: asking one AI | Model Council: comparing several |
|---|---|
| Only one line of reasoning; errors are easy to miss | Multiple lines of reasoning can be cross-checked against each other |
| Hard to notice where a model's strengths and weaknesses don't fit the task | Differences in summarization, reasoning, coding, etc. show up directly as agreement or disagreement |
| Reading other models' answers means asking again | The unified view and each individual answer sit on the same screen |
What to do next
Expect more high-stakes questions to get routed to more than one model.
The Model Council approach is likely to spread to other AI products over time. Here are three things readers can do right now.
- For questions tied to important decisions, don't stop at one AI's answer — cross-check it through a tool that can run several models side by side.
- In a unified view, pay more attention to where models disagree than where they agree, and dig into why (differing training data, differing strengths, and so on).
- Always check each model's cited sources, rather than trusting the synthesizer's summary alone.
Still, this isn't unqualified good news. As The Register's headline wryly put it, "tokenmaxxing" is exactly what happens when 2 to 8 models run in parallel on every query — compute cost and token consumption scale right along with it. And agreement across multiple models doesn't guarantee factual correctness: models with similar training data and similar biases can converge confidently on the same mistake. Model Council visualizes where opinions diverge; it does not guarantee which opinion is right.