Referral Rock vs Viral Loops
Seven of twelve models named Referral Rock first on the direct prompt; zero named Viral Loops. Referral Rock was named by twelve of the twelve models and Viral Loops by ten and Referral Rock carries 34 labels and Viral Loops 20, so the shares are not 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 referral and partner programs page.
By framing
Referral RockFirst choices, of twelve modelsViral Loops
Across every category in the September 2026 Edition, Referral Rock and Viral Loops were named in the same answer thirty times, of the 148 answers naming Referral Rock and the 50 naming Viral Loops. In those answers Viral Loops took the first choice three times and Referral Rock three.
Every model, every framing
| 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 | | |
Referral Rock Viral Loops first choice named as an alternative argued againstblank: not namedEach cell is one answer, Referral Rock on the left and Viral Loops on the right.The direct prompt
Referral Rock first, Viral Loops not the choice
7 of 12 modelsViral Loops was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
4 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
1 of 12 modelsThe answer made no first choice from these two in this category.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.
By buyer segment
Referral Rock17%#1 of 12Viral Loops0%#10 of 12Referral Rock15%#2 of 11Viral Loops4%#5 of 11Referral Rock6%#4 of 13Viral Loops0%#– of 13What the models said about Referral Rock
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“ReferralRock – $175/mo (budget flex if you have it)” Qwen 3.7 Flash · budget prompt · soft negative
“Referral Rock offers the best balance of B2B-specific features, CRM integrations, transparent pricing, and scalability” Kimi K2 · direct prompt · first choice
“If you want a quick launch with robust CRM tracking and high-quality advocate portals, choose Referral Rock.” Gemini 3.5 Flash · direct prompt · first choice
What the models said about Viral Loops
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Multiple reports of billing issues ... 1-star reviews highlight "deceptive practices" and "waste of money."[” Grok 4.1 Fast · negative prompt · hard negative
“aimed at B2C viral loops and pre-launch waitlists, not ongoing B2B referral operations” DeepSeek V4 Flash · direct prompt · hard negative
“*Caution for cash reward programs* — No automated fraud detection” DeepSeek V4 Flash · negative prompt · soft negative
“Start with Viral Loops - it offers the best combination of affordability ($35/month), comprehensive features, and a solid free trial period.” GLM 4.7 FlashX · budget prompt · first choice
“ReferralCandy or Viral Loops offer the best balance of features and ease of use” MiniMax M2.5 · comparative prompt · first choice
“Viral Loops (≈$35/month) is the best all-around value” DeepSeek V4 Flash · budget prompt · first choice
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.