TL;DR
GPT-5.5, a large proprietary model, produces significantly more hallucinations than the open-source GLM-5.2, highlighting limits of increasing model size for true intelligence.
Recent tests confirm that GPT-5.5, a proprietary large language model estimated at 1-2 trillion parameters, hallucinates three times more often than MIT-licensed GLM-5.2, which has 753 billion parameters. This discrepancy questions the assumption that bigger models inherently produce more accurate or truthful outputs, a topic of growing concern among AI researchers and industry stakeholders.
In recent comparative evaluations, GPT-5.5 demonstrated an 86% hallucination rate on the AA-Omniscience benchmark, significantly higher than GLM-5.2’s 28%. Despite GPT-5.5’s estimated size of 1-2 trillion parameters, it failed to produce more reliable answers, often confidently asserting false information. Conversely, GLM-5.2, with roughly 40 billion active parameters, achieved better factual accuracy and lower hallucination rates, even though it is substantially smaller.
Experts attribute GPT-5.5’s high hallucination rate to its inability to recognize logical and technical fallacies, especially in complex reasoning tasks. Tests involving technical Python questions revealed that GPT-5.5 confidently provided incorrect solutions, whereas smaller models like GLM-5.2 identified the errors quickly. These findings suggest that increasing parameter count alone does not improve model reliability and may, in fact, exacerbate hallucination issues.
Industry analysts warn that this trend indicates a plateau in true AI intelligence, emphasizing the need to balance model size with calibration, uncertainty handling, and computational efficiency. The current trajectory of scaling models may lead to diminishing returns in accuracy and increased risks of misinformation.
Implications for AI Development and Trustworthiness
The higher hallucination rates of GPT-5.5 compared to smaller, open-source models challenge the notion that larger models are inherently better at producing truthful responses. This raises concerns about deploying massive proprietary models in real-world applications where accuracy is critical. The findings highlight the importance of focusing on model calibration, uncertainty management, and efficiency rather than solely increasing size, especially as AI moves closer to general intelligence.
For users and developers, this underscores the need to evaluate models beyond benchmark performance and consider their reliability in practical scenarios. The industry must reconsider the emphasis on scale and prioritize approaches that improve factual accuracy and reduce hallucinations to build trustworthy AI systems.
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Limitations of Scaling and Recent Model Comparisons
Over the past few years, the AI community has largely equated larger models with better performance, driven by the belief that more parameters and training data lead to greater intelligence. However, recent developments, including restrictions on models like Claude Fable 5 and the emergence of open-source alternatives such as GLM-5.2, have begun to challenge this paradigm.
In June 2026, comparative tests between proprietary models like GPT-5.5 and open models like GLM-5.2 revealed that size alone does not guarantee accuracy. While GPT-5.5 is estimated to be 1-2 trillion parameters, it exhibits a much higher hallucination rate than GLM-5.2, which has 753 billion parameters. These results suggest that the industry is reaching a plateau in the effectiveness of simply scaling models and should instead focus on improving their reasoning and calibration capabilities.
“GPT-5.5’s hallucination rate is alarmingly high, despite its size, indicating that bigger models are not inherently more truthful.”
— an anonymous researcher
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Unconfirmed Aspects of Model Performance and Future Trends
While the recent tests provide strong evidence of hallucination disparities, it remains unclear whether these results generalize across other tasks and datasets. The exact reasons behind GPT-5.5’s high hallucination rate are still being investigated, and the impact of training data quality, model architecture, and calibration techniques requires further study. Additionally, the long-term implications of these findings for AI development and regulation are still uncertain.
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Next Steps in AI Model Evaluation and Development
Researchers and industry leaders are expected to conduct broader evaluations across diverse tasks to confirm these initial findings. Efforts will likely focus on developing models with better uncertainty calibration, reducing hallucination rates, and balancing size with efficiency. Regulatory and safety considerations may also influence future model training and deployment strategies, emphasizing reliability over raw scale.
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Key Questions
Why does GPT-5.5 hallucinate more than smaller models?
According to recent tests, GPT-5.5’s large size may contribute to overconfidence and difficulty recognizing logical errors, leading to higher hallucination rates. Its inability to say ‘I don’t know’ also exacerbates this issue.
Does bigger always mean better in AI?
No. Recent evidence shows that larger models can produce more hallucinations and less reliable answers, suggesting that size alone is not a sufficient indicator of performance or truthfulness.
What are the risks of deploying high-hallucination models?
Models with high hallucination rates can generate false or misleading information, which poses risks in applications requiring high accuracy, such as healthcare, legal advice, and safety-critical systems.
Will the industry shift away from scaling models?
Many experts believe so. The recent findings suggest a need to focus more on model calibration, efficiency, and factual accuracy rather than solely increasing parameters.
What can be done to reduce hallucinations in large models?
Improving uncertainty calibration, training on higher-quality data, and developing techniques for better logical reasoning are potential strategies to reduce hallucinations in future models.
Source: Hacker News
