Writing fingerprint analysis of responses reveals Kimi's similarity to Claude
This article explores a technical analysis of LLM writing styles, specifically comparing Kimi and Claude using fingerprinting techniques. It includes code snippets and methodology for measuring linguistic similarity between models.
Why it matters
Understanding the distinct 'fingerprints' of AI models helps researchers identify model origins and potential training data overlaps.
You're relatively right! Which LLM models write alike? A heat map built from their words alone.
--- format: typebulb/v1 name: "You're relatively right!" ---
```tsx import React, { useEffect, useMemo, useState } from "react"; import { createRoot } from "react-dom/client"; import { encode } from "gpt-tokenizer/encoding/cl100k_base";
// ── Types ────────────────────────────────────────────────────────
testId: string; testName: string; subject: string; score: number;
response: string; response2?: string; reasoning: string; error?: string; }; type RawData = { timestamp?: string; judge?: string; results: RawResult[] };
type MetaRow = { match: string; lab: string; released: string | null };
type Counts = { map: Map<string, number>; n: number }; type Bg = { words: Counts; phrases: Counts; common: Set<string> };
// "<true lab>: <model>" — OpenRouter entries resolve to their real lab
released: string | null; // YYYY-MM, from the data block; null until supplied
phraseDf: Map<string, number>; // #answers containing each bigram
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