2 categories · Named, not rankedVector
Named, too few times to rank.
A product needs 10 labels in a category before a share is stated. Vector has 3.
Why models recommend it
No positive label carried a quote.
What gives them pause
“Products such as RB2B, Vector, and Warmly can have narrower geographic coverage”Perplexity Sonar · Negative question · warning
“may have limited adoption or integration capabilities”Mistral Small · Negative question · warning
Also on the shortlist
The products named in the same answers as Vector, over the 5 answers that named it, and how often each was the first choice instead.
| Product | Named together | Picked instead | |
|---|---|---|---|
| 4 | 1 | View → | |
| 4 | 1 | View → | |
| 2 | 2 | View → | |
| 2 | 0 | View → | |
| 2 | 0 | View → | |
| 2 | 0 | View → | |
| 2 | 0 | View → | |
| 2 | 0 | View → | |
| 2 | 0 | View → | |
| 2 | 0 | View → |
A side-by-side page exists where both products are among a category's top eight.
How this is measured
Fourteen AI models from 12 labs are asked the same buying questions six ways, for three company sizes. Standings refresh every edition.
Gane sells AI visibility software. No company can pay to change a result.
Read the method →Sources returned with these answers
Fourteen of the fourteen models returned a source list in this edition: five return the pages they cited and nine the search results they consulted. These counts come from 5 of the 5 answers that named Vector and are not a share of its labels.
Domains cited
Twenty of the twenty domain citations in answers naming Vector came from a third party's page.
Pages cited
Pages are listed as the models cited them.
By model
| Model | First choice | Alternative | Mention | Warning | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 0 | 0 |
| Perplexity Sonar | 0 | 0 | 0 | 1 | 1 |
| Grok 4.20 | 0 | 0 | 0 | 0 | 0 |
| Mistral Small | 0 | 0 | 0 | 1 | 1 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 0 | 0 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 0 | 0 | 0 | 0 |
| Kimi K2 | 0 | 0 | 0 | 0 | 0 |
| GLM 4.7 FlashX | 0 | 0 | 1 | 0 | 1 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 0 | 0 | 0 | 0 |
What it publishes
| Kind | Pages | Last 90 days | 2025-10 to 2026-09 | Latest | Categories named |
|---|---|---|---|---|---|
| Blog | 972 | undated | SEO, Content marketing | ||
| Comparison | 21 | undated | SEO, CRM | ||
| Glossary or explainer | 13 | undated | SEO | ||
| Template or tool | 6 | undated | SEO |
How it is countedHide how it is counted
Every page vectoron.ai exposes, subdomains included. Kind is read from the address and title. The last 90 days, the latest date and the twelve months count pages by when they were published, from the site's feeds, a date in the address, or the page's own publication date, read from up to a hundred of its most recently changed pages; a page that says only when it last changed is counted in its kind but not in when, so the recent counts are a floor, and a kind none of whose pages gives a publication date reads undated. Of its titles that name a category, most name SEO (322); 0 name its categories the models place it in. Read September 29, 2026.