GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2 (arrowtsx.dev)

Article summary

A recent study compared the performance of several large language models, including GPT-5.5 and GLM-5.2, and found that the larger models tend to hallucinate more, or provide confident but incorrect answers. The study suggests that the pursuit of bigger models may not be the best approach, as they can become less accurate and more prone to hallucination. The article argues that the industry should focus on developing models that can recognize their own limitations and say 'I don't know' when they are unsure.

Main themes

• Large language models
• Hallucination rates
• Model size and accuracy
• AI development strategies
• Code generation and maintenance

What commenters say

• The high hallucination rates of large language models are a significant problem that needs to be addressed, as they can provide confident but incorrect answers.
• The pursuit of bigger models is not the best approach, as they can become less accurate and more prone to hallucination.
• LLMs can be useful for generating code, but their output should be thoroughly reviewed and tested to ensure accuracy and maintainability.
• The quality of code generated by LLMs is comparable to that of human developers, and may even be better in some cases, but it can still introduce technical debt and require significant maintenance.
• The use of LLMs to generate code can be beneficial, but it is not a replacement for human developers and should be used as a tool to assist and augment their work.
• The focus on developing larger models may be misguided, and instead, the industry should prioritize developing models that can recognize their own limitations and say 'I don't know' when they are unsure.
• The comparison of LLM-generated code to human-written code is unfair, as human-written code can also be of poor quality, especially in large, complex systems.
• The use of LLMs to generate code can lead to a false sense of security, as the generated code may seem decent at first but can introduce subtle bugs and maintenance issues over time.