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 with the next one
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.
For each dated snapshot 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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