1 category · Named, not rankedRegal
Standing by category
| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| AI voice agents | Customer | 3% | 9 of 103 | 0% | 3 | under 10 labels · led by Retell AI at 27% |
Movement
By model
How each model treated Regal across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.ShowHide
By model
| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-5.4 mini | 1 | 0 | 0 | 0 | 1 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 0 | 0 |
| Perplexity Sonar | 0 | 0 | 0 | 0 | 0 |
| Grok 4.20 | 0 | 1 | 0 | 0 | 1 |
| Mistral Small | 0 | 0 | 0 | 0 | 0 |
| DeepSeek V4 Flash | 1 | 0 | 0 | 0 | 1 |
| 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 | 0 | 0 | 0 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 0 | 0 | 0 | 0 |
By framing
What the models said for it
Verbatim evidence the judge attached to positive labels.
“Regal is probably the best "mid-sized B2B" balance of flexibility, operational seriousness, and being purpose-built for voice automation” GPT-5.4 mini · AI voice agents · paraphrase prompt · first choice
“or Regal if your primary focus is outbound B2B sales” DeepSeek V4 Flash · AI voice agents · direct prompt · first choice
“Pure sales/outbound: Regal (B2B lead qual/meeting booking; conversion-focused)” Grok 4.20 · AI voice agents · direct prompt · alternative
And against it
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
Named alongside
The products named in the same answers as Regal, over the 7 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Regal was named but was not.ShowHide
Named alongside
What carried it into the answer
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 7 of the 7 answers that named Regal and are not a share of its labels.
Domains cited
Thirty-nine of the thirty-nine domain citations in answers naming Regal came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Follow Regal
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
Claim this page
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Regal's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Regal, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
What a new claim receivesHide what a new claim receives
A new claim receives the current edition's vendor brief for Regal by email, built from the raw record of the edition. It shows:
- where Regal is named, by buyer and by framing, and which cells hold its first choices;
- the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
- its vocabulary against the segment leader's, and the pages the models cited;
- who was chosen in the answers that did not name Regal, and every reason the record gives;
- a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
- one page of published figures cleared to show a buyer.