Referral Factory vs Friendbuy
One of fourteen models named Referral Factory first on the direct prompt; one named Friendbuy. Referral Factory was named by eight of the fourteen models and Friendbuy by thirteen and Referral Factory carries 13 labels and Friendbuy 23, so the shares are not directly comparable.
Named in three categories this edition.
Palos Verdes Estates, United States, founded 2010. Named in two categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen 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
Referral FactoryFirst choices, of fourteen modelsFriendbuy
Across every category in the October 2026 Edition, Referral Factory and Friendbuy were named in the same answer forty-two times, of the 138 answers naming Referral Factory and the 127 naming Friendbuy. In those answers Friendbuy took the first choice one time and Referral Factory seven.
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 | | |||||
| GPT-6 Luna | | |||||
| Muse Glimmer 30B | |
Referral Factory Friendbuy first choice named as an alternative argued againstblank: not namedEach cell is one answer, Referral Factory on the left and Friendbuy on the right.The direct prompt
Referral Factory first, Friendbuy not the choice
1 of 14 modelsFriendbuy was named in the answer but not as the choice, or not at all.Friendbuy first, Referral Factory not the choice
1 of 14 modelsReferral Factory was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
1 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
11 of 14 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 Factory15%#3 of 9Friendbuy0%#9 of 9Referral Factory4%#6 of 14Friendbuy2%#8 of 14Friendbuy6%#5 of 17Referral Factory0%#– of 17What the models said about Referral Factory
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“Common reasons to avoid / be cautious” Muse Glimmer 30B · negative prompt · hard negative
“Users report slow technical support, time-consuming setup, and limited integration options.” Mistral Small · negative prompt · soft negative
“I'd usually recommend Referral Factory as the first place to look.” GPT-5.4 mini · paraphrase prompt · first choice
“the best choice is usually Referral Factory” GPT-5.4 mini · direct prompt · first choice
What the models said about Friendbuy
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“High-Friction Enterprise Platforms with Opaque Pricing & Long Contracts (e.g., Friendbuy, SaaSquatch, Annex Cloud) ... generally ill-suited for growing businesses” Muse Glimmer 30B · negative prompt · hard negative
“Premium pricing without proportional value... Competitors offer similar core features for 60-90% less.” Kimi K2 · negative prompt · hard negative
“has opaque pricing and long contracts, making it ill-suited for growing businesses” Llama 4 Maverick · negative prompt · hard negative
“Influitive and Friendbuy are the most highly recommended” Mistral Small · direct 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.