AI image generators
Text-to-image models and products for marketing, product and concept imagery. Which ones do AI engines actually name when buyers ask?
60 answers · 10 buyer questions · 3 engines
First snapshot — movement appears next week
Only 4 of the top 10 are sourced from their own site. The other 6 are described to buyers by third-party pages.
Third-party sources: reddit.com (Midjourney) · aimlapi.com (DALL·E) · designrush.com (Stable Diffusion) · neolemon.com (Leonardo AI) · teachaitools.blog (FLUX) · aiunpacker.com (Canva)
| # | Product | Named in answers | Page engines cite for it |
|---|---|---|---|
| 1 | 48% ±12 29/60 | ||
| 2 | 48% ±12 29/60 | ||
| 3 | 33% ±12 20/60 | ||
| 4 | 28% ±11 17/60 | ||
| 5 | 15% ±9 9/60 | ||
| 6 | 15% ±9 9/60 | ||
| 7 | 12% ±8 7/60 | ||
| 8 | 10% ±8 6/60 | ||
| 9 | 10% ±8 6/60 | ||
| 10 | 10% ±8 6/60 |
“Named” is the share of the 60 answers that recommend the product by name, with a 95% interval — read #10 as “a handful of answers”, not a position. “Own site cited” is the share where the product’s own domain was a source. The page column is where the engines actually got their answer — attributed per product, from the citation the answer attaches to it.
Every week we ask 10 buyer questions to 3 engines, multiple runs each, through their official APIs. Products are extracted from the answers themselves and folded to one row per product (plans and tiers are not separate products). Products under 6 answers sit in “also named” — their positions aren’t distinguishable on this sample.
No sponsored rows, featured slots or paid placements — ever. The only way up is to be the answer. Run the check on your domain to see which answers you’re missing from, and why.
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