GTM AI Index
Index Marketing Loyalty › Referral Rock vs GrowSurf
Loyalty, advocacy and referrals · September 2026 Edition

Referral Rock vs GrowSurf

Three of twelve models named Referral Rock first on the direct prompt; two named GrowSurf. Referral Rock was named by nine of the twelve models and GrowSurf by ten and Referral Rock carries 23 labels and GrowSurf 14, so the shares are not directly comparable.

Referral Rock

accepted challenger

Named in two categories this edition.

GrowSurf

accepted challenger

Named in two categories this edition.

First-choice share22%5%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#4A position in a field of 18; printed, not drawn.
Labels2314A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Referral Rock reading right to left. Rank and label count are printed, not drawn.Smile.io vs Referral Rock · Smile.io vs GrowSurf · Referral Rock vs Influitive

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.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
Referral RockFirst choices, of twelve modelsGrowSurf
Direct32
Paraphrase60
Comparative00
Budget-constrained00
Scale-constrained00
Negative00
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Across every category in the September 2026 Edition, Referral Rock and GrowSurf were named in the same answer sixty times, of the 148 answers naming Referral Rock and the 91 naming GrowSurf. In those answers GrowSurf took the first choice four times and Referral Rock nineteen.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Referral Rock and GrowSurf stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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 GrowSurf first choice named as an alternative argued againstblank: not namedEach cell is one answer, Referral Rock on the left and GrowSurf on the right.

The direct prompt

The plain question, one answer per model, grouped by where Referral Rock and GrowSurf stood in it.

Both were the first choice

1 of 12 modelsThe answer named them together, and the judge labeled each a first choice.
MiniMax M2.5GrowSurf, Referral Rock alternatives: FirstPromoter, Kademi, Zinrelo

Referral Rock first, GrowSurf not the choice

2 of 12 modelsGrowSurf was named in the answer but not as the choice, or not at all.
Gemini 3.5 FlashReferral Rock alternatives: Annex Cloud, Influitive, PartnerStack, SaaSquatch
Kimi K2Referral Rock, Zinrelo alternatives: Kademi, PartnerStack

GrowSurf first, Referral Rock not the choice

1 of 12 modelsReferral Rock was named in the answer but not as the choice, or not at all.
Mistral SmallGrowSurf alternatives: PartnerStack, True Loyal

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.
Claude Haiku 4.5CustomerGauge alternatives: GrowSurf, Referral Rock, Salesforce Loyalty Management, Zinrelo
Grok 4.1 FastInfluitive alternatives: Ambassador, Referral Rock, TrueLoyal
DeepSeek V4 FlashAmbassador alternatives: CustomerGauge, PartnerStack, Referral Rock
Llama 4 Maverickno first choice alternatives: GrowSurf, Referral Rock

Neither was named

4 of 12 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniAmbassador alternatives: CustomerGauge, Referral Factory, Salesforce Loyalty Management
Perplexity SonarCustomerGauge, Open Loyalty alternatives: Talon.One
Qwen 3.7 FlashInfluitive alternatives: Ambassador, CustomerGauge
GLM 4.7 FlashXTrue Loyal alternatives: Annex Cloud, Open Loyalty, Yotpo Loyalty & Referrals

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Referral Rock leads by thirty points.
Referral Rock30%#2 of 11
GrowSurf0%#6 of 11
The full small business standing →
Mid-marketThe figures above
Referral Rock leads by seventeen points.
Referral Rock22%#2 of 18
GrowSurf5%#4 of 18
The full mid-market standing →
Enterprise
Referral Rock leads by two points.
Referral Rock2%#10 of 18
GrowSurf0%#– of 18
The full enterprise standing →

What the models said about Referral Rock

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.

“Top Pick: Referral Rock ... highly regarded for mid-market B2B companies because it is built to handle the complexity of longer B2B sales cycles” Gemini 3.5 Flash · direct prompt · first choice
“For most mid-sized B2B companies, I'd recommend starting with Referral Rock for its focused referral capabilities and reasonable pricing” Mistral Small · paraphrase prompt · first choice

What the models said about GrowSurf

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
Also compared

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.