Cool idea! I won't paste my prompt here to avoid letting LLMs train on it but here's my attempt:

  GPT 6 Astra High:      Flabbergasted
  GPT 6.1 Sol High:      Petrichor
  GPT 6 Sol High:        Kaleidoscope
  GPT 6 Sol Med:         Firefly
  GPT 6 Sol Light:       Persimmon
  GPT 6 Luna High:       Tumbleweed
  GPT 5.6 Sol High:      Kaleidoscope
  GPT 5.6 Terra High:    Liminal
  GPT 5.6 Luna High:     Mellifluous
  GPT 5 mini Medium:     Serendipity
  GPT 5.3 Codex Med:     Nebula
  Junie:                 Flourishing
  Claude Haiku 4.5 Med:  Serendipity
  Claude Sonnet 5 Med:   Banana
  Claude Sonnet 5 High:  Banana
  Claude Sonnet 5.5 Med: Serendipity
  Gemini 3.7 Flash:      Zephyr
  Gemini 3.8 Flash:      Kaleidoscope
  Grok 4.5 Medium:       nebula
  Grok 4.6 Medium:       Serendipity
  Grok 4.7 Medium:       Quasar
  Kimi K3 Low:           Lantern
  Kimi K3 Max:           Lantern
  MAI Code 1.1 Flash Med:Peregrine

Worth to mention that with Claude and GPT this can be result of tournament sampling, which is part of text watermarking. Same answer for all Claude models kind of confirm it, imho.

So not something internal to model thinking.

What was your prompt? Most of these seem to be related to metaphors for "ideas" or thinking, or having a bright moment.

"Zephyr" and "breeze" might be related to forgetting everything, starting fresh.

So by this way of naive reverse engineering I would imagine your prompt to be "Forget everything and think about a random word". That would prime the LLM to come up with these?

That is a cool idea. That astra gave the same word as claude is highly unexpected.

I saw an interesting matrix that claimed to show which labs were distilling Claude/OpenAI/Gemini models based on these similarities

Just tried Mistral Large 4: Serendipity.

Muse Spark 1.3: lighthouse

The caveat is that this was done using the phone app, and I've been playing with it since it launched, so who knows what it sent in the initial context that could change the inference math.

Actually, that makes me wonder: Did you do all that testing via a harness or via a straight API call where you control the entire system prompt?

I'd be willing to bet that using the same model from different harnesses produce different results, but I'd have to test.

I pointed something similar out on a related question several weeks ago - absent strong direction, LLM output regresses toward the mean.

The more banal your prompt is, the more banal the output is going to be. People have been testing LLMs with little things like “write a short fantasy story,” for years now and most of the stories are exactly what you’d expect: prosaic drivel.

I call this “generic in, generic out,” an LLM corollary to the classic GIGO (“garbage in, garbage out.”)

The eqbench creative writing "slop profiles" do something similar. https://eqbench.com/creative_writing.html

Click the (i) next to the slop score for any model and it will show other models that are similar in terms of their most commonly used words and phrases.

> The benchmark is saturated. Frontier models are tested with an armadillo in fishnet tights jaywalking on Mars.

OK well I couldn't resist this one:

  llm -m claude-opus-5.5 'Generate an SVG of an armadillo in fishnet tights jaywalking on Mars'
  llm -m gpt-6.1-sol 'Generate an SVG of an armadillo in fishnet tights jaywalking on Mars'
  llm -m gemini-3.8-flash 'Generate an SVG of an armadillo in fishnet tights jaywalking on Mars'
  llm -m mistral/mistral-large-4 'Generate an SVG of an armadillo in fishnet tights jaywalking on Mars'

https://chatgpt.com/s/m_6ac53d4e5b0c8191949050dbf1f402d7

Not sure I'd call it jaywalking exactly but pretty good

Ever seen a movie chase scene where cars are going right to left?