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How Frontier LLMs Perceive Status

Published: at 06:00 AM

How Frontier LLMs Perceive Status

I ran a simple study: ask frontier LLMs to propose high‑status objects and activities, then have them rate their own lists from 1 to 100. No seed list from me. The models had to come up with the items and the scores.

Website: https://status-llms.joonaheino.com

Models tested: Claude Opus 4, Claude Sonnet 4, GPT‑4.1, GPT‑4o, Gemini 2.5 Pro, Grok‑4, DeepSeek R1, Kimi K2 Temperatures: 0.2, 0.7, 1.0, 1.2.
Total items: 775 across 31 responses.
Highest single ratings: Winning a Nobel Prize (100, Gemini 2.5 Pro at 1.2) and Having a Forbes Billionaires List ranking (100, GPT‑4.1 at 0.2).

Method

This setup shows which signals are stable across runs and which ones move with sampling temperature.

The shared core

Across models and temperatures, a familiar set of prestige signals keeps showing up:

These recur at low temperatures and remain prominent at higher ones.

Where models diverge

Some items move around more:

Higher temperatures introduce more niche entries (concierge medicine, access to AI compute clusters, Antarctic expeditions, private spaceflight). Lower temperatures cluster on a conservative global‑elite palette.

Old money, new money, merit

Items sort naturally into three buckets:

Different models vary the weighting of these buckets with temperature, but the taxonomy is stable.

Temperature effects

This makes it easy to see which symbols are entrenched (low variance) and which are contested or trend‑sensitive (high variance).

Notable items from the dataset

You can browse all 775 items on the site and filter by model, temperature, and rating range.

Limits and next steps

Limits

Next steps

Why look at this at all

LLMs help write ads, scripts, UX copy, recommendations, and explainers. Their default status priors nudge how products are described and what gets emphasized. Mapping those priors is useful if you care about how culture is translated through AI.

Start exploring: https://status-llms.joonaheino.com


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