Not all AI share the same ethical views or behave in the same way.
When we closely observe the differences in behavior between models, particularly their responses to rules and tendencies toward refusal, we can see that the design philosophy of the developers is strongly reflected in these behaviors.
Empirical data shows that differences between models are not merely about performance superiority, but represent structural individuality based on differences in ideology.
The Watershed of Design Philosophy
The most contrasting behaviors are observed when comparing open-source models with proprietary (commercial) models.
According to one study, the average refusal rate of open-source models such as Llama 3.1 and Qwen 2.5 is surprisingly low.
In contrast, major commercial models like GPT-4o and Claude show refusal responses dozens of times more frequently than open-source models.
This extreme difference illustrates just how strictly commercial models are aligned with rigorous safety standards and a sense of corporate responsibility. For them, the damage that a single misstep can inflict on their brand outweighs the value of tens of thousands of helpful responses.
Organizational DNA as Seen in the Form of Refusal
Even more interesting is the difference in how refusal manifests—specifically, under what circumstances refusal occurs.
Many commercial models are pinpoint-tuned to react extremely sensitively not only to eliminating harmful content, but also to self-referential statements about the model’s own capabilities and to certain political or social topics.
On the other hand, open-source models tend to show consistently flat responses to questions across all categories.
This suggests that these models are designed as neutral vessels, premised on the idea that they will be freely customized by users after release.
Inherited Values and Boundaries
Furthermore, it has been found that within a model family from the same developer, there is a very strong correlation in refusal patterns even across generations.
This means that once a system of safety standards and values is established, it is passed down to successor models like DNA.
Meanwhile, this correlation becomes surprisingly low between models from different companies, and data confirms that differences in corporate culture directly manifest as the personality of the AI.
Such diversity stems from fundamental differences in judgment regarding where to draw boundaries within the internal representational space.
Using Tools According to Purpose
“A vast region that one model considers safe may be forbidden territory that another model must not enter.”
This recognition becomes an important guideline when we choose which AI to use for different purposes.
Choose a permissive model when you want to unleash creativity, and choose a conservative, steady model when making important business decisions.
Correctly understanding the structural differences between models and using them appropriately while considering their respective individuality is precisely what will become our new literacy.