Every AI Has Its Own Coding Style, New Research Finds

Most comparisons of AI coding assistants come down to a score: did the code work, yes or no? That was useful when models were clearly unequal. But many AI models now pass the same basic tests, so a simple pass-or-fail score tells you less and less. A group of researchers asked a different question: forget who wins — how do these models actually behave when they write code?

Their answer is a tool called CLIC. It breaks each piece of generated code into "tokens" — the small chunks of words and symbols an AI reads and writes, roughly like syllables. Each model leaves a distinct pattern of favorite tokens, almost like a handwriting signature. The system then uses a decision tree (a simple flowchart of yes/no questions) to tell two models' code apart, and measures two things: how stubborn that difference is, and whether it comes from a few telltale tokens or is spread across many.

To test it, the team compared 10 language models across 22 real machine-learning tasks from Kaggle, a site where data scientists compete on problems. The differences were real and measurable. The payoff is practical: if you know Model A tends to write code in one style and Model B in another, you can pick the assistant that fits your project, or word your instructions differently to get what you want.

The honest catch is that this is academic research, not a product. It requires technical setup, it doesn't declare a single "best" model, and its findings can age quickly because AI models are updated constantly. Think of it as a new microscope for comparing AI coders — useful for companies and researchers, not yet a button you can press.