Model Council: every frontier model, one query.

When to useUse Model Council when: • The answer matters (high-stakes decision, customer-facing claim) • You suspect a single model might be biased or wrong • You want to catch hallucination by seeing disagreement • You're researching something where models might have very different training data • You're building an opinion and want to test it against multiple AI perspectives Use a single model when: • It's a quick question or a draft • You already know which model is best for this task • Speed matters more than depth • You're iterating on something where multi-model is overkill

Reading disagreementsModel Council interpretation patterns: • **All models agree on facts, vary on emphasis** → trust the facts, develop your own opinion • **Models disagree on a specific fact** → don't trust ANY of them; check primary sources • **One model has more recent info than others** → use the most recent (Perplexity flags this) • **One model is dramatically more confident than others** → be suspicious of the confident one • **Models disagree on tone or interpretation** → see this as range of valid views, not as wrong-vs-right The goal: use Model Council to surface uncertainty, not to resolve it. Disagreement is signal that you need human judgment.

Pre-publication checkPre-publication check for [the claim you're about to make publicly]: Ask Model Council: • 'Is this claim accurate based on current information?' • 'What are the strongest counter-arguments?' • 'What context is missing that would change interpretation?' • 'Are there recent (2026) developments that should be acknowledged?' • 'Would a domain expert push back on this claim, and how?' If all models confirm: ship with confidence. If models disagree: revise to acknowledge uncertainty, or research further. If any model raises a concern none of the others did: investigate that specific concern.