# LoyaltyLion vs Friendbuy: which do AI models recommend for loyalty, October 2026

GTM AI Recommendation Index, October 2026 Edition, Loyalty, advocacy and referrals. Zero of fourteen models named LoyaltyLion first on the direct prompt; one named Friendbuy. Page: https://gtm-ai-index.com/marketing/loyalty-advocacy-and-referrals/loyaltylion-vs-friendbuy/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| LoyaltyLion | 2% | #7 of 14 | 23% | 22 | 12 of 14 |
| Friendbuy | 2% | #8 of 14 | 30% | 23 | 13 of 14 |

## The direct prompt, model by model

- Mistral Small: friendbuy first (first choices: Friendbuy, Influitive AdvocateHub) (alternatives: LoyaltyLion, Yotpo Loyalty & Referrals)
- Llama 4 Maverick: neither first, one named (alternatives: Friendbuy, ReferralCandy, Yotpo Loyalty & Referrals, Zinrelo)
- Claude Haiku 4.5: neither named (first choices: Kademi) (alternatives: Annex Cloud, PartnerStack, Referral Rock, TrueLoyal)
- GPT-5.4 mini: neither named (first choices: Referral Factory) (alternatives: Ambassador)
- Gemini 3.5 Flash: neither named (first choices: Influitive AdvocateHub) (alternatives: Ambassador, Cello.so, FirstPromoter, PartnerStack, Referral Rock)
- Perplexity Sonar: neither named (first choices: Enable3) (alternatives: Antavo, Gainsight, Open Loyalty)
- Grok 4.1 Fast: neither named (first choices: Referral Rock) (alternatives: Ambassador, Influitive AdvocateHub)
- DeepSeek V4 Flash: neither named
- Qwen 3.7 Flash: neither named (first choices: Exponent) (alternatives: Referral Rock, Tnt1)
- Kimi K2: neither named (first choices: Referral Rock) (alternatives: Ambassador, Influitive AdvocateHub)
- GLM 4.7 FlashX: neither named (first choices: Cello) (alternatives: Annex Cloud, CustomerGauge, SlapFive)
- MiniMax M2.5: neither named (first choices: Enable3) (alternatives: GrowSurf, Influitive AdvocateHub, SlapFive, Zinrel0)
- GPT-6 Luna: neither named (first choices: SlapFive) (alternatives: Influitive AdvocateHub, ReferenceEdge, SaaSquatch by impact.com)
- Muse Glimmer 30B: neither named (first choices: Referral Rock, Smile.io) (alternatives: Ambassador)

## What the models said about LoyaltyLion

- "Aggressive pricing escalation... Annual contracts with no exit clause: Hard to leave once locked in" (Kimi K2, negative prompt, hard negative)
- "Also flagged by AI models for potential risks and not recommended for enterprise use." (Mistral Small, negative prompt, hard negative)
- "these are the leading e-commerce loyalty apps, and both are excellent products. However, they feature specific structural traps" (Gemini 3.5 Flash, negative prompt, soft negative)
- "LoyaltyLion: Best-in-class for mid-market Shopify/BigCommerce brands, with exceptional Klaviyo integration." (Gemini 3.5 Flash, scale prompt, first choice)
- "Aim for tools like LoyaltyLion, Yotpo, Smile.io, ReferralCandy, or Voucherify, which balance features and cost for your scale." (Grok 4.1 Fast, scale prompt, alternative)
- "Best for: Mid-market e-commerce brands ... wanting more robust, data-driven loyalty." (DeepSeek V4 Flash, comparative prompt, alternative)

## What the models said about Friendbuy

- "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)

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. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.