Comment by comboy
8 hours ago
I recently was testing something, I asked some models to provide me a single random word:
claude-opus-5: Lantern
claude-opus-5-5: Lantern
claude-fable-5-1: Lantern
claude-fable-5: Lantern
gemini-3.8-flash: Zephyr
gemini: Petrichor
qwen3.5-dashscope: Zephyr
glm-5.1: Lantern
gpt-6-astra: Lantern
grok-4: octopus
mimo-v2.5-pro: Breeze
minimax-m2.5: serendipity
kimi2.6-or: Gossamer
grok-4.20: luminescent
deepseek-v4-flash: serendipity
deepseek-v4-pro: Endurance
deepseek-chat: Serendipity
I have enough projects, I think some benchmark/dashboard showing kinship based on these kind of queries could be very interesting to watch and insightful when new models come out.
Cool idea! I won't paste my prompt here to avoid letting LLMs train on it but here's my attempt:
I really like this idea. You could expand on this by giving programming tasks and measuring code similarity. Seems like you could develop a pretty detailed understanding of similarities across multiple queries.
> You could expand on this by giving programming tasks and measuring code similarity.
But the same coding task should usually result in very similar code since they have a reason to converge, to some extent, by having the same goal. I would even claim that the code will be more similar as competence increases. It would be better to pick something that shouldn't have a reason to converge.
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Just tried M365 Copilot with a premium account. Petrichor
Just tried Space Bunny and it gave me the same word...
I got Peregrine out of GPT-6 too. Huh.
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.
This feels uncannily like the ancestor of the Voight-Kampff test[0]
0: https://www.youtube.com/watch?v=Umc9ezAyJv0
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That is a cool idea. That astra gave the same word as claude is highly unexpected.
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?
just “a random word” gives you Zephyr in Gemini, and “Lantern” in Claude and ChatGPT.
Lantern in Sonnet 5.5
I got "Marmalade" in Claude (Opus 5.5)
I got pomegranate in ChatGPT
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.”)
Of course one of the biggest problems we still see with LLMs is when you do the opposite. A highly detailed unique prompt is very likely to get terrible adherence or hallucination or both.
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.
Tried this with gpt-5.6-sol. Lantern!
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.
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.