> "Models with >75% writing similarity but massive price gaps. The cheap model writes the same way. You are paying for the brand.

* > ...*

* > Gemini 2.5 Flash Lite Preview 06-17 and Claude 3 Opus: 78.2%*

As someone who has tried to use many of these models for writing assistance, you're very wrong here. It really matters whether the model can get what I'm trying to communicate well enough to be helpful, or else I'll just write it myself. If you actually play with them a bit it's very clear these models are not substitutes. This goes for many on your list!

It would be shocking to me if the large model trainers didn't have tools like this to analyze their outputs, but this is interesting work!

You can see who likely (post)trained/distilled their models or borrowed parameters from each other. I do wonder if the 32 dimensions were chosen/named from principal components or pre-selected and designed, but the tool seems like an effective discriminator in any case.

Were the prompts similarly selected for orthogonality? I've wondered how the different LLMs would respond from iterative zero-shot prompt_n generation by summary from a previous response_n to generate zero-shot response_n+1. Would it statistically converge to a more distinguishable prompt for that LLM?

I've always wondered if the "typical" AI writing style is just an unavoidable RL artifact or a deliberate fingerprint to prevent model collapse as low-effort AI-generated text floods the training data pool (the web).

Assuming this is part of the reason I constantly see "--" whenever I prompt Claude or ChatGPT

Ugh. subheadings were a major turn off.

I expected it to be an analysis of AI-generated writing styles. Not full of them.

;)

This is really cool. That's the good stuff. Did you notice any pattern in why models cluster? Shared training data or just similar architecture choices?

No mention of any linguistic theory, some arbitrary (?*) metrics mixed together and even more arbitrary thresholds. Why does 75% "similarity" mean "writes the same"?

Low quality post imo.

*Generated I assume.

The muted colors on a dark background makes everything hard to read.

Besides claiming opus and gemini flash share 99% of style being suspicious, the point that you are wasting money on the expensive model is non sensical. You pay primarily for the intelligence, not the writing style.

Is this article AI slop?

Amazing,last time I let GPT guess Claude content,it guess GPT made it

Without showing the prompts and responses, it's yet another meaningless AI benchmark.

Many of those numbers do not really match what I've seen in the wild, and without clear illustration why you arrived at the number it's not a helpful number.

I'm curious about the sorts of users who care about style but will either one-shot with default style, not providing samples or direction, or who even choose models on that style rather than, you know, substance.

Has anyone else used LLMs to fact check other LLMS?

I hate to say it, but Gemini lies less frequently than paid models from OPenAI and Anthropic (Open AI is worst in my use cases).

My guess is that Google has better training data (and uses less synthetic data which might be creating training feedback loops in other models), has more of a "be calibrated" model than a "be helpful" model, but it could just be that they leverage more RAG than leveraging weights more.

But, I really shouldn't speculate the "why" as I'm out of my domain. Just curious if others use all the models they can and compare outputs as much as I do.