Quick answer: When up to six AI models gave the same answer, it was right 94.0% of the time (1,148 of 1,221 questions). It was also wrong 73 times. Agreement is a useful signal, not proof, so treat disagreement as a reason to check.
What we tested
On 22 October 2026 we put 2,500 multiple-choice questions with known answers to up to six AI models. The questions came from MMLU-Pro, TruthfulQA, PubMedQA and GPQA Diamond. Each model answered every question once, with a single option letter. Five models answered 1,965 questions, six answered 533, and four answered 2.
What we found
- Agreement tracked correctness. Unanimous answers were right 94.0% of the time. When agreement fell below 60%, the majority answer was right only 33.5% of the time.
- Unanimity was still wrong 73 times. That is 6.0% of unanimous questions: 44 in MMLU-Pro, 15 in TruthfulQA, 9 in PubMedQA and 5 in GPQA Diamond. Every answering model gave the same wrong letter.
- The majority did not clearly beat the best single model. On the 2,303 questions with a clear majority, the majority scored 77.1% and Claude alone scored 77.5%.
So the value of asking several models is not a higher score. It is that you can see how much they agree, where they split, and when a check is worth the time.
What this does not show
This was an exploratory test on letter-only multiple-choice questions, not on free-form answers. It does not show that any product display changes what people decide, and it does not test whether models fail for the same reasons. Four of the six model identifiers were undated aliases, and 63.9% of the questions came from MMLU-Pro. Method, limits and data are on the full benchmark page.
Seeing disagreement for free
You do not need paid models to look at disagreement. Satcove Open sends one question to several free open-weight models, which answer separately, and shows where they agree and where they do not. We did not run the benchmark above on Open's free models, so the 94.0% figure does not describe Open. Use it the same way, though: agreement tells you where to relax a little, and a split tells you where to verify.
If the answer matters (health, money, law), check a primary source whatever the models say. Our guide on when not to use multi-AI consensus covers the cases where this approach does not help.
Frequently asked questions
Does AI agreement mean the answer is correct?
No. In our test, unanimous answers were right 94.0% of the time, but 73 of 1,221 unanimous answers were wrong. Agreement raises confidence without removing shared blind spots, so check anything that matters against a primary source.
Is a majority of AI models more accurate than the best single model?
Not clearly. On the 2,303 questions with a clear majority, the majority scored 77.1% and Claude alone scored 77.5%. With ties counted as failures the majority scored 71.0%. The value of several models is the visible agreement level, not a higher score.
What should I do when AI models disagree?
Treat the split as a trigger to verify. When agreement fell below 60%, the majority answer was right only 33.5% of the time. Look at which models differ, then check a primary source before acting.
Read the data
The page includes the answers file, the recovered-cell list, the claims table and the analysis scripts, with checksums. We do not redistribute question texts.