Comparison & ranking
Generative-AI try-on vs AR, model photos, and size charts
Five ways to "try before you buy" online, ranked for how well they help you judge fit and look before purchase. Below is the full scoring table, the ranking, and a visible methodology so you can see exactly how we got there.
Last updated June 2026 · wearfits.me editorial
Side-by-side comparison
| Method | Realism | Shows it on YOU | Works for any garment | Effort | Confidence before buying |
|---|---|---|---|---|---|
| Generative-AI try-on | High — photorealistic | Yes, your body | Yes — most categories | Low — one photo | Highest |
| AR mirror / overlay | Medium — stylized | Yes, live | Limited — AR-ready items | Medium — camera setup | Medium |
| Static model photo | High — but not you | No — a fit model | N/A | None | Low–Medium |
| Size chart only | None — numbers | No | N/A | Low — measure once | Low |
| No preview (nothing) | None | No | N/A | None | Lowest |
Ratings are qualitative judgments based on how each method works, not lab measurements. See the methodology below.
The ranking
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1
Generative-AI virtual try-on
Renders the real garment on your own body, photorealistically, across most categories — from a single photo or your height and size. It directly answers "does it fit me and how do I look," so it scores highest on confidence for the lowest effort.
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2
AR mirror / overlay try-on
Shows a live overlay on you through the camera, which is engaging and good for movement. But results are stylized rather than photorealistic, and coverage is limited to garments specifically prepared for AR — so it's better for fun than for judging true fit and drape.
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3
Static on-model photos
Real, high-quality photos — but of a fit model who usually isn't your body type. You have to mentally translate the look onto yourself, and that gap is exactly where "it didn't look the way I expected" returns come from.
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4
Size chart only
Accurate measurements help you avoid the worst sizing mistakes, but there's no picture — you have to imagine the cut, drape, and overall look entirely from numbers. Useful as a backstop, weak on its own.
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5
No preview at all
Buying from a thumbnail and a size label, with nothing to judge fit or look. It's the fastest at checkout and the most expensive in returns and disappointment.
Methodology
This is a qualitative editorial comparison, not a benchmark study. We scored each method against five criteria that map to what a shopper actually needs when deciding whether to buy:
- Realism — how closely the preview resembles a real photo of the garment being worn.
- Shows it on YOU — whether the visual is of your own body, versus a model or no body at all.
- Works for any garment — how broad the coverage is across tops, bottoms, dresses, outerwear, and shoes.
- Effort — what the shopper has to do (upload a photo, set up a camera, take measurements, nothing).
- Confidence before buying — how much the method reduces the "will this fit and look right?" uncertainty.
Ratings reflect how each approach works in general, drawn from the capabilities each method is designed to deliver and from common shopper experience. They are not lab measurements, and individual implementations vary — an AR feature or a model-photo set can be better or worse than the typical case described here. The ranking orders methods by overall ability to help a shopper judge fit and look before purchase, weighting "shows it on you," realism, and confidence most heavily, since those are what reduce the expectation gap that drives apparel returns.
The "shows it on you" methods aren't just rated higher here — they perform better in published reports. Virtual advisors and try-on convert at roughly 12% vs 3% for traditional ecommerce, and Zalando reported a 40% drop in jeans returns after rolling out virtual try-on (Savills, 2026); 55% of shoppers have returned clothing because it looked different on them than expected (eMarketer, 2025) — exactly the gap the top-ranked method closes.
For the data behind why that expectation gap matters, see our apparel returns data page. Capability claims for generative-AI try-on reflect the product documented in our live demo.