alt Hacker NewsI 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
Replies
This feels uncannily like the ancestor of the Voight-Kampff test[0]
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.
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
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?
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.”)
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.
Cool idea! I won't paste my prompt here to avoid letting LLMs train on it but here's my attempt: