[OC] Each of 13 LLMs has a measurable linguistic fingerprint - drawn across 8 syntactic patterns Visualization

What This Visualization Shows

This data visualization displays "[OC] Each of 13 LLMs has a measurable linguistic fingerprint - drawn across 8 syntactic patterns" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on **Tool**: built it myself, open source - [github.com/thirdshiftlab/modeltell](http://github.com/thirdshiftlab/modeltell)

**Data**: outputs from 13 LLMs - frontier (Claude Opus 4.6/4.7/4.8, GPT-5.5, Gemini 3.1 Pro), mid-tier (Claude Sonnet 4.6, GPT-5.4 Mini, Gemini 3.5 Flash), and open-weight (DeepSeek V3.2 & V4 Pro, Llama 4 Maverick, Mistral Large, Qwen3.7 Max). Each model answered the same 30 prompts × 3 runs (\~90 generations, \~22k words per model). The reference each model is scored against is the aggregate of all the other models, not a human corpus - a matched human baseline isn't built yet.

**Method**: measured how often each model overuses specific words and 8 syntactic patterns (tricolons, hedging openers, em-dash pivots, power-verb stacking, etc.) relative to the rest of the set; each model's profile is drawn as a "fingerprint" radar across those axes. "Overuse" = how much more frequently a model uses a word/pattern than the average across all 13 models, per 1,000 words (words ranked TF-IDF-style so distinctive ones surface, not just common ones).

**Full interactive version (EN + DE) with a paste-your-own-text linter**: [https://modeltell.com](https://modeltell.com), which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

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Data Analysis and Insights

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Significance and Applications

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