Evaluating 35 open-weight models across three context lengths (32K, 128K, 200K), four temperatures, and three hardware platforms—consuming 172 billion tokens across more than 4,000 runs—we find that the answer is “substantially, and unavoidably.” Even under optimal conditions—best model, best temperature, temperature chosen specifically to minimize fabrication—the floor is non-zero and rises steeply with context length. At 32K, the best model (GLM 4.5) fabricates 1.19% of answers, top-tier models fabricate 5–7%, and the median model fabricates roughly 25%.
As I pointed out in another root comment, the average - depending on the model being tested - tends to sit between 60% and 80%. But this is with no restriction on source materials… the LLMs are essentially pulling from world+dog in that case
So this opens up an interesting option for users, in that hallucinations/inaccuracies can be controlled for and potentially reduced by as much as ⅔ simply by restricting the model to those documents/resources that the user is absolutely certain contains the correct answer.
I mean, 25% is still stupidly high. In any prior era, even 2.5% would have been an unacceptably high error rate for a business to stomach. But source-restriction seems to be a somewhat promising guardrail to use for the average user doing personal work.
Thanks for providing the actual numbers.
I think one of the more concerning things is, what if you think the answer is in the documents you provided but they actually aren’t. What you think is a low error rate could actually be a high error rate.