The End of Giant AIs? GPT-5.5's Hallucination Rate Exceeds GLM-5.2 by Over 3 Times. The Shockwaves from Z.ai's New Model

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The End of Giant AIs? GPT-5.5’s Hallucination Rate Exceeds GLM-5.2 by Over 3 Times. The Shockwaves from Z.ai’s New Model

What Happened? News Overview

GPT-5.5’s Hallucination Rate Hits 86%: In a startling revelation, GPT-5.5 has been shown to provide false information more than three times as often as the open-weight (MIT licensed) GLM-5.2, which has a hallucination rate of 28%.
Stagnation of Giant Model Intelligence: While GPT-5.5 and Opus 4.8 boast parameter counts in the trillions, the 753 billion parameter GLM-5.2 is closing the performance gap, highlighting the diminishing returns of simply scaling up models.
Government Regulation on Claude Fable 5: Just three days after its release, the U.S. government imposed usage restrictions, citing “national security risks.” The security vulnerabilities and risks associated with giant models have been laid bare.

Why Does This Matter? Key Points to Consider

The Myth of “Bigger is Smarter” is Crumbling: While the 1.6 trillion parameter DeepSeek V4 Pro wasted over three minutes grappling with a logical inconsistency in complex Python, the half-sized GLM-5.2 identified the correct answer—“technically impossible”—in just 12 seconds.
Escalation of Hallucinations: Larger models are increasingly unable to admit when they don’t know something, leading to a stronger tendency to confidently assert falsehoods. DeepSeek V4 Pro has reached a staggering hallucination rate of 94%.

🦈 Shark’s Eye (Curator’s Perspective)

This news signals that the big sharks of the AI world are starting to sink under their own weight, folks! What’s particularly shocking is that GLM-5.2 was able to instantly spot inconsistencies in Python single-thread processing. While the massive GPT-5.5 and DeepSeek V4 Pro blindly nodded “yes” to the impossible task of delivering packages to three houses without stopping, GLM-5.2 calmly pointed out, “That’s impossible!” This proves that simply piling on data and parameters won’t lead us to “true logical thinking.” An open model under MIT license is biting back at the black-box giants in terms of accuracy—this is true underdog spirit!

What’s Next?

The industry trend is shifting completely from “bigger” to “more accurate and efficient.” Moving forward, resolving the “AI Trilemma” (capability, uncertainty calibration, and computational efficiency) will be the top priority, and users will start choosing models based not only on size and benchmark numbers but also on their real-world integrity.

A Final Word from Haru Shark

A big shark that can’t maneuver is just a liability! From now on, smart and agile models like GLM-5.2 will rule the seas! Shark shark!

Terminology Explained

Hallucination: The phenomenon where AI generates information that is not based on facts, presenting it as if it were true. Also referred to as “hallucinations.”
Open-weight Model: An AI model where the weights (trained data) are publicly available, allowing anyone to use or modify it. GLM-5.2 falls into this category.
AI Trilemma: The challenge in AI development of simultaneously meeting the three criteria of “high processing capability,” “low hallucination rate (accurate self-assessment),” and “computational efficiency.”
Source: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2