GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2
A comparative analysis suggests that smaller, open-weight AI models like GLM-5.2 are becoming more efficient and accurate than massive proprietary models like GPT-5.5. The study highlights that larger models often suffer from higher hallucination rates when forced to provide answers.
Why it matters
This trend challenges the 'bigger is better' paradigm in AI development, suggesting a shift toward efficiency and factual reliability.
A shift is happening among major AI labs, who are becoming increasingly skeptical of endless parameter count and training data scaling. The limits of this paradigm were put on the world’s stage when Claude Fable 5 was restricted by the US government just three days after its release, marking the first US AI ban stemming from national security. One of the biggest models in the world was banned because a single jailbreak was too much of a risk.
The article presents technical benchmarks and industry observations, though it leans into the skepticism currently popular in the AI research community.
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