Published August 9, 2026
There's an old, boring, extremely effective idea for dealing with unreliable sources: don't ask one. Ask several independent ones and compare. Journalists call it corroboration. Engineers call it redundancy. Aviation builds it into every critical system.
Applied to AI, the same idea has a name: consensus checking. Ask multiple language models the identical question, line up the answers, and read the pattern of agreement instead of trusting any single response.
Consensus checking is the practice of sending one question to several independently developed AI models and comparing their answers before relying on any of them. Where the models converge, the answer earns more confidence. Where they contradict each other, the disagreement itself is the finding: it marks the question as one where a single confident answer would have misled you.
The method doesn't make any individual model smarter. It changes what you're reading: from one answer of unknown reliability to a distribution of answers whose spread you can see.
Because the models' errors don't fully overlap, provided the models are genuinely independent.
Each major lab trains on its own data mix, with its own architecture, its own tuning choices, and its own knowledge cutoff. When a model hallucinates, the specific fabrication comes from the interaction of that question with that model's particular training. A different model, trained differently, is unlikely to invent the same fabrication. So while any one model confidently inventing a citation is common, five independently inventing the same citation is far less so.
Agreement across independent models is therefore evidence (not proof, evidence) that the answer reflects something stable in the underlying information rather than one model's improvisation.
Independence is the load-bearing word. Asking the same model twice, or asking two products built on the same underlying model, gives you correlated answers and false comfort. Real consensus checking needs models from different labs. That's why a proper lineup spans companies and even countries: 8legs runs each question across ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), Grok (xAI), Llama (Meta), DeepSeek, Kimi (Moonshot AI), and Qwen (Alibaba). Eight models, eight separate training pipelines. (Where each provider receives your question, including which models run via the US hosting service Replicate, is documented in the FAQ and privacy policy.)
Reading eight full answers yourself defeats the purpose; you'd spend more time comparing than you saved asking. So after all models answer, a separate AI pass reads the complete set of responses and produces a short summary: what most models agree on, any notable differences, and how many models share the majority view. Contradictions get flagged explicitly, so you know which answers to double-check before acting, and you can open any individual model's answer and continue the thread with it directly.
The output you actually read is small: the majority position, the outliers, and the count. “7 of 8 agree; one model contradicts on the dosage threshold” tells you in one line whether this question is settled enough to act on or split enough to escalate.
Three honest limits, and if you're evaluating any multi-model tool, including ours, these are the questions to press on.
Models can be wrong together. If the training data most models share contains an error, or the question concerns something after most models' cutoffs, the majority can converge on the same wrong answer. Agreement measures independence-weighted plausibility, not ground truth. Eight yeses is strong evidence; it is not proof.
The consensus layer is itself an AI. The summarizing pass can misread nuance in the underlying answers, treating two compatible answers as contradictory, or glossing over a subtle but important difference. The per-model answers stay visible precisely so the summary never has to be taken on faith.
It can't do the final verification for you. Consensus checking tells you where to look closer: which answers are contested, which are stable. For medical, legal, financial, or tax decisions, that triage is genuinely valuable and genuinely insufficient. Nothing shown by any AI tool substitutes for a qualified professional, and any tool that implies otherwise is overselling.
We hold ourselves to that framing: as we put it on our About page, when eight independently trained models agree, the answer deserves more confidence, and agreement still isn't proof.
Consensus checking is corroboration, industrialized: one question, several independent models, and a readable map of where they agree and where they split. Use the agreements as earned confidence, use the contradictions as a to-verify list, and keep professionals in the loop for anything where wrong is expensive. It's not magic. It's just the oldest trick in dealing with unreliable sources, finally cheap enough to use on every question.