Collective AI Reasoning is a methodology where several AI models (GPT, Claude, Gemini, Grok, Perplexity, Llama, Mistral, DeepSeek, Kimi) work on one pro/con argument tree for a yes/no question and then rate every argument. Argumentree.AI runs it in turn: models with web search go first, the first model builds the tree, and each next model reviews the arguments already on it and adds its own, with evidence. Then every model rates every argument, which reveals what they agree on and where they diverge. When most models rate an argument up, you have higher confidence. When they disagree, you've found a genuinely contested question worth investigating. This approach makes errors easier to spot (an argument the other models cannot support gets low ratings, which flags it for you to check), reduces one-sided answers (several models trained on different data review the same tree), and keeps reasoning transparent (every argument and every rating shows which model it came from). Argumentree.AI is available at argumentree.ai with plans starting from free.
Stop trusting one AI's opinion. With Collective AI Reasoning, several models build and review one argument tree, then rate every argument — revealing what they agree on.
Experience Collective AI ReasoningPose a yes/no research question on any topic — policy, science, strategy, law. Example: 'Should the EU adopt a carbon border tax?'
The models take turns: web-search models (Perplexity, GPT, Grok, Gemini, Claude) go first and bring sources; the first builds the pro/con tree, each next model reviews it and adds its own arguments with evidence.
Every model rates every argument. The ratings surface the strongest claims, and an argument the other models cannot support gets low ratings, which flags it for you to check.
See which arguments all models agree on (high confidence) and which are controversial (worth investigating). Consensus = confidence.
Collective AI Reasoning is when several AI models (GPT, Claude, Gemini, Grok, Perplexity, and more) take turns building and reviewing one pro/con argument tree for a yes/no question, then rate every argument. When most models agree, you have higher confidence. When they disagree, you've found a genuinely contested question.
Models with web search: Sonar Pro (Perplexity), GPT-5.4 Mini (OpenAI), Grok 4.1 Fast (xAI), Gemini 2.5 Flash Lite (Google) and Claude Sonnet 4 (Anthropic). Other models: Llama 3.3 70B, Mistral Large 3, DeepSeek V3.2 and Kimi K2.5 on Azure AI in Sweden, and Grok 4 Fast (xAI). GPT, Claude, Gemini, Perplexity and Grok are optional and run at their vendors outside the EU. Each brings different training data and reasoning styles to the Collective AI Reasoning process.
After the models have built and reviewed the tree in turn, every model rates every argument up or down — the arguments of the other models and its own. Every rating shows which model gave it. The ratings show which claims are broadly agreed upon and which are controversial.
It makes them easier to spot. When one model invents a claim, the other models are unlikely to support it when they rate it, so it gets low ratings, which flags it for you to check. It is not a guarantee — models can share the same mistake — so check the sources of any argument you rely on.
Collective AI Reasoning excels at complex, contested questions where multiple valid perspectives exist — policy analysis, legal questions, scientific hypotheses, business strategy, and journalism and fact-checking. Simple factual lookups don't benefit as much from consensus.
Experience Collective AI Reasoning — free to start.
Start Free Research