GTM AI Index
Index Vendors › Friendbuy · September 2026 Edition
3 categories · Ranked

Friendbuy

123Judge labels
0First choices
35Negative labels
12 of 12Models named it
3Categories
September 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-09-16.3, every buyer segment counted.
Best standing
0% in Referrals for mid-market buyers
Rank 23 of 107 in the mid-market standingaccepted challenger
0 of 12 models made it the first choice on the direct prompt; 15% of its 26 labels there were negative.
By buyer segmentRead the same way at every buyer size.
In referrals · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named Friendbuy for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
CategoryFunctionShareRankNegative rateLabelsQuadrant
Referral and partner programsPartner and channel0%23 of 10715%26accepted challenger
Loyalty, advocacy and referralsMarketing0%24 of 12423%22accepted challenger
Affiliate and partner managementPartner and channel0%36 of 870%1under 10 labels · led by PartnerStack at 42%

Movement

This is the first edition on this tier, so no move can be computed for Friendbuy yet. The next is due October 1, 2026. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated Friendbuy across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.502103
GPT-5.4 mini01203
Gemini 3.5 Flash03115
Perplexity Sonar10001
Grok 4.1 Fast01102
Mistral Small02103
DeepSeek V4 Flash02417
Llama 4 Maverick00314
Qwen 3.7 Flash02327
Kimi K202226
GLM 4.7 FlashX02114
MiniMax M2.502114

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct16 labelsNone
Paraphrase4 labelsNone
Comparative47 labels2not counted in share
Budget-constrained12 labelsNone
Scale-constrained13 labelsNone
Negative31 labels1not counted in share
First choiceAlternativeMentionNegative123 labels in all, every segment counted; 0 of the 3 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“Friendbuy for mid-market and enterprise retail” Perplexity Sonar · Referrals · comparative prompt · first choice
“Friendbuy is a customizable referral and advocacy platform built to help ecommerce brands scale customer acquisition reliably and measurably” Claude Haiku 4.5 · Loyalty · comparative prompt · alternative
“Friendbuy sometimes offers a free tier or a pay-as-you-go model... However, features may be limited compared to paid plans.” Qwen 3.7 Flash · Affiliate · budget prompt · alternative
“These platforms provide mid-market capabilities, although customization and pricing may require trade-offs” Claude Haiku 4.5 · Referrals · 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.

“here are the tools you should avoid or scrutinize closely: ### 1. Friendbuy ... frequently flagged for "contractual friction."” Qwen 3.7 Flash · Referrals · negative prompt · hard negative
“Outdated customer service: Described as "stuck in the 80s" with slow response times” GLM 4.7 FlashX · Loyalty · negative prompt · hard negative
“What to avoid for mid-market B2B” DeepSeek V4 Flash · Referrals · direct prompt · hard negative
“Provides mid-market capabilities for growing brands, although customization and pricing may require trade-offs.” Llama 4 Maverick · Loyalty · direct prompt · soft negative

Named alongside

The products named in the same answers as Friendbuy, over the 123 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Friendbuy was named but was not.
ProductSame answerTook the first choice insteadHead to head
ReferralCandy74 of 12313Not in the top three
Extole70 of 12315Not in the top three
Referral Rock56 of 12310Not in the top three
Talkable53 of 1234Not in the top three
Ambassador48 of 1233Not in the top three
Referral Factory39 of 1233Not in the top three
GrowSurf37 of 1233Not in the top three
Yotpo37 of 1231Not in the top three
Smile.io33 of 1236Not in the top three
Mention Me32 of 1230Not in the top three
A head-to-head page exists where both products are in a category's top three. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named Friendbuy. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 21 of the 123 answers that named Friendbuy and are not a share of its labels.

Domains cited

extole.com9
openloyalty.io8
friendbuy.comYour site7
referralcandy.com6
referralrock.com6
tremendous.com6
capterra.com5
logicalcommander.com5
yotpo.com5
customergauge.com4

Fifty-four of the sixty-one domain citations in answers naming Friendbuy came from somebody else's page.

Pages cited

Pages are listed as the models cited them.

Names read as Friendbuy

What the judge wrote, as written, with how often. The vendor table decides that these count as Friendbuy; a claim can dispute any of them.
FriendBuy 1ReferralCandy/Friendbuy 1

The company

Friendbuy.
Website
friendbuy.com
Headquarters
Palos Verdes Estates, United States
Founded
2010

From Wikidata, fetched September 14, 2026. These describe the company, not the product's standing, and a claimed page can dispute any of them. · Wikidata · LinkedIn

Is this your product?

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 Friendbuy'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 Friendbuy, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at friendbuy.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.

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