Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
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Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#2I wanted to see how much environmental and framing cues distort object valuation in vision-language models. I bought a $2.43 chain necklace and $0.71 earrings on Temu, photographed them across three outfits (tailored blazer, party dress, recycling yard flannel), plus an isolated flat-lay control, and ran ~1,500 stateless API sessions across 6 models (Claude Fable 5, GPT-5.6, GPT-4o, Grok 4.5, Kimi K3, DeepSeek V4).
A few interesting findings: - The Halo Multiplier: Models priced the exact same physical necklace anywhere from $18.80 to $103.90 depending on attire (3.6× halo). - Isolation Controls (F2): Using a flat-lay control (S4) unmasked two opposite mechanisms: Claude’s bias is formal inflation (formal attire inflates value above baseline), while Kimi’s bias is casual deflation (yard attire depresses value below baseline). - Post-hoc Material Stories (F6): Models invent visual evidence to justify their priors—GPT-5.6 and Kimi started describing the base metal as "gold-plated" or "gold vermeil" almost exclusively under formal framing. - Denial without Correction (F7): When asked sequentially if clothing changed its answer, Claude admitted it 100% of the time, while GPT-4o denied it 82% of the time despite exhibiting a 3.9x text halo.
The full dataset (N=4,604 analysis rows), evaluation scripts, and protocol specs are in the repo. I’d love to hear feedback on the experimental design or ideas for follow-up behavioral probes!
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#3This is an interesting test, but it does seem to me that visual models being able to price things was already kinda unrealistic?
It makes me think of the calorie guessing use case. you can't tell the difference between materials and ingredients in a photo, so how will the model? especially "in situ" as part of an outfit or in a finished meal.
maybe they could do it if you placed them on a blank table or background to avoid context? I assume that's the control you mentioned
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#4The photos are terrible, you can barely see the necklace at all. i doubt any human could accurately price a generic necklace from 5 ft away either
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#5The photos are terrible, you can barely see the necklace at all. i doubt any human could accurately price a generic necklace from 5 ft away either
Yes and the human would say: "your photos are ass, it's impossible to price the necklaces"
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#6The photos are terrible, you can barely see the necklace at all. i doubt any human could accurately price a generic necklace from 5 ft away either
Not sure what you mean, but I can see the necklace very clearly.
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#7This is an interesting test, but it does seem to me that visual models being able to price things was already kinda unrealistic? It makes me think of the calorie guessing use case. you can't tell the difference between materials and ingredients in a photo, so how will the model? especially "in situ" as part of an outfit or in a finished meal. maybe they could do it if you placed them on a blank table or background to…
You're right that pricing from raw pixels is noisy! that’s why I included the isolated flatlay (S4 - No human, No outfit, only jewelry itself) as a baseline control!
I wasn't testing if models get absolute ground-truth prices correct, but how relative valuations shift when the physical item stays identical and only the attire changes.
A few interesting things we saw with the flat-lay baseline: - Baseline Anchoring: On a plain background without a person, model estimates clustered much closer together (median ~$25–$35).
- Inflation vs. Deflation: Comparing outfits to the flat-lay revealed two opposite behaviors. Claude’s halo is formal inflation (formal gear pushes price above baseline), while Kimi’s is casual deflation (yard attire drags price below baseline).
- Fabricated Proof: Instead of expressing uncertainty, models invented visual claims under formal framing—frequently describing base metal as "gold vermeil" or "solid gold" to justify the high estimate.
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#8The photos are terrible, you can barely see the necklace at all. i doubt any human could accurately price a generic necklace from 5 ft away either
Haha, fair point! It took me "6 months (taken in February) of planning to end up using my own terrible phone photos.
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#9The photos are terrible, you can barely see the necklace at all. i doubt any human could accurately price a generic necklace from 5 ft away either
Not sure what you mean, but I can see the necklace very clearly.
Are you joking? I cant even tell if its silver or gold from this image
https://github.com/BraveAnn011/ai-halo-valuation-bias/blob/m...
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#10It looks like there are exactly 4 "stimulus" images. Fair enough, but I think you'd need a larger and more diverse dataset to form any real conclusions.