Two of twelve models named GrowSurf first on the direct prompt; one named Open Loyalty. GrowSurf was named by ten of the twelve models and Open Loyalty by nine and both carry 14 labels, so the shares below are directly comparable.
Named in two categories this edition.
Named in two categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the loyalty, advocacy and referrals page.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.1 Fast | ||||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 |
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“I'd recommend Referral Rock or GrowSurf as your top choices” MiniMax M2.5 · direct prompt · first choice
“Best Overall for Mid-Market B2B: GrowSurf” Mistral Small · direct prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“Open Loyalty is the best-fit option among the results for mid-market and enterprise companies” Perplexity Sonar · direct prompt · first choice
“API-first, composable toolkit for custom builds” Perplexity Sonar · comparative prompt · alternative
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.