We may be asking the wrong question about AI hallucinations.
“Which model hallucinates the least?”
It sounds like a reasonable question.
But after looking across the major benchmarks, the numbers make the problem obvious: reported hallucination rates range from 0.7% to 100%.
Not because one benchmark is necessarily right and another wrong. Because they are measuring different failure modes, under different conditions.
Grounded tasks are not open-knowledge tasks. Summarizing a supplied document is not recalling case law. And a model that refuses aggressively may look safer while becoming significantly less useful.
So perhaps the more important question for organizations deploying AI is: What happens when the model is wrong — and the process cannot detect it?
That is the question behind our research at KeyWow. We reviewed the 2025–2026 evidence across hallucination benchmarks, grounding, abstention, RAG, web-enabled systems and high-risk domains.
One conclusion stands out: Hallucination is not just a model problem. It is a process-risk problem.
We put the findings together in a new executive report: Hallucination rates in large language models.
For anyone making decisions about AI at scale, I think the numbers are worth a look.