CollectivIQ wants to make AI more reliable by querying 14 models at once

The hallucination problem in language models remains one of the main barriers to mass trust in artificial intelligence. Anyone who has ever received confidently presented but completely fabricated information from ChatGPT knows this feeling: the technology impresses, but trusting it blindly is dangerous. The startup CollectivIQ decided to attack this problem from an unexpected angle — not by improving a single model, but by querying fourteen of them simultaneously.

The idea, which TechCrunch reported on, is elegant in its simplicity. CollectivIQ aggregates answers from ChatGPT, Gemini, Claude, Grok, and up to ten other language models at the same time. A user enters a query once and receives a panorama of answers, where they can compare formulations, identify points of agreement and, more importantly, notice disagreements. If thirteen out of fourteen models say one thing and one says another, that's a powerful signal. If the models disagree evenly, that's also valuable information: it means the question is more complex than it seems, and blindly trusting any single answer is not worth it.

To understand why this approach might work, it's worth recalling a phenomenon that decision theory calls the "wisdom of crowds." Francis Galton discovered back in 1906 that the averaged assessment of a large group of people turns out to be more accurate than the opinion of any single expert. CollectivIQ essentially transfers this principle to the world of large language models.

Each of them is trained on different data, with different emphases and limitations. GPT-4o is strong in reasoning, Claude — in accuracy and instruction-following, Gemini — in multimodality and working with current information, Grok — in informal tone and access to data from social networks. When their answers are combined, the weaknesses of one model are compensated by the strengths of another.

Technically, implementing such a service raises several serious questions. First and foremost — cost. Each request to a commercial API costs tokens, and multiplying by fourteen turns kopecks into noticeable sums.

For an ordinary user who asks for a borscht recipe, this is excessive. But for professionals — lawyers, doctors, analysts, journalists — for whom accuracy is critically important, the economics could work out. The second question is speed.

Parallel requests to different APIs have different response times, and the user will either have to wait for the slowest model or receive answers asynchronously, as they arrive. The third is interface design. Presenting fourteen answers in a way that a person doesn't get lost in information but quickly extracts the essence — that's a serious design challenge.

CollectivIQ doesn't appear in a vacuum. The market already has AI model meta-search engines: Quora's Poe provides access to several models in a single interface, and services like TypingMind and OpenRouter allow switching between providers. But none of them are betting on simultaneous comparison as a verification tool. CollectivIQ positions multimodality not as convenience, but as a method of increasing reliability — and that's a fundamentally different narrative. Instead of "choose the best model" — "don't trust any single one, compare all of them."

There's also a deeper context. The artificial intelligence industry is experiencing a crisis of trust. Research shows that users are increasingly skeptical of chatbot responses, but continue to use them — simply because there are no alternatives. CollectivIQ offers an intermediate solution: it doesn't force blind trust and doesn't require abandoning AI, but gives you a tool for critical analysis. In a sense, this is a return to the journalistic principle of cross-checking sources, except the sources are neural networks.

The main question is whether this model scales. If major providers begin to restrict API access for aggregators or raise prices, CollectivIQ's business will be threatened. Moreover, as models become increasingly similar to each other, learning from overlapping datasets, the value of multimodel comparison may decrease. But as long as diversity of approaches remains, the idea of crowdsourcing among AIs looks both clever and practical. Perhaps the future of reliable artificial intelligence is not one perfect model, but a choir of imperfect ones, where a false note is immediately heard.