Smile.io vs Referral Factory
Zero of twelve models named Smile.io first on the direct prompt; zero named Referral Factory. Smile.io was named by twelve of the twelve models and Referral Factory by eight and Smile.io carries 36 labels and Referral Factory 12, so the shares are not directly comparable.
Named in three categories this edition.
Named in three 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.
By framing
Smile.ioFirst choices, of twelve modelsReferral Factory
Across every category in the September 2026 Edition, Smile.io and Referral Factory were named in the same answer twenty-six times, of the 115 answers naming Smile.io and the 116 naming Referral Factory. In those answers Referral Factory took the first choice two times and Smile.io 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 | |
Smile.io Referral Factory first choice named as an alternative argued againstblank: not namedEach cell is one answer, Smile.io on the left and Referral Factory on the right.The direct prompt
Neither was the first choice, one was named
1 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
11 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
Smile.io33%#1 of 11Referral Factory11%#3 of 11Smile.io24%#1 of 18Referral Factory5%#6 of 18Smile.io2%#12 of 18Referral Factory2%#– of 18What the models said about Smile.io
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of six in this category shown.
“Billing complaints: Multiple merchants report being charged even after cancelling their free plan” GLM 4.7 FlashX · negative prompt · hard negative
“Billing disputes (charges post-cancel, free plan limits eroding, overcharges on order counts); support delays on refunds” Grok 4.1 Fast · negative prompt · soft negative
“various tiers of Smile.io) have been flagged by users for aggressive pricing behaviors” Qwen 3.7 Flash · negative prompt · soft negative
“Smile.io (Best for $0 Budget) ... the only major loyalty platform with a permanently free plan that actually includes referrals” MiniMax M2.5 · budget prompt · first choice
“Smile.io stands out as the best customer loyalty and referral platform for companies with a limited budget.” Grok 4.1 Fast · budget prompt · first choice
“The best customer loyalty and referral platform for a company with a limited budget is Smile.io” Llama 4 Maverick · budget prompt · first choice
What the models said about Referral Factory
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Lack of multi-channel support (Referral Factory) ... No dedicated support for the Starter plan” Llama 4 Maverick · negative prompt · soft negative
“I'd generally recommend Referral Factory if your primary goal is to launch and scale a customer referral program quickly” GPT-5.4 mini · paraphrase prompt · first choice
“I'd recommend Referral Factory if your main goal is to launch and scale a customer referral program quickly” Perplexity Sonar · paraphrase 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.