Ambassador vs Friendbuy
Zero of fourteen models named Ambassador first on the direct prompt; one named Friendbuy. Ambassador was named by ten of the fourteen models and Friendbuy by thirteen and Ambassador carries 15 labels and Friendbuy 23, so the shares are not directly comparable.
Named in two 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
AmbassadorFirst choices, of fourteen modelsFriendbuy
Across every category in the October 2026 Edition, Ambassador and Friendbuy were named in the same answer forty-six times, of the 102 answers naming Ambassador and the 127 naming Friendbuy. In those answers Friendbuy took the first choice three times and Ambassador 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 | | | |
Ambassador Friendbuy first choice named as an alternative argued againstblank: not namedEach cell is one answer, Ambassador on the left and Friendbuy on the right.The direct prompt
Friendbuy first, Ambassador not the choice
1 of 14 modelsAmbassador was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
6 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
7 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
Ambassador0%#– of 9Friendbuy0%#9 of 9Ambassador4%#5 of 14Friendbuy2%#8 of 14Friendbuy6%#5 of 17Ambassador4%#7 of 17What the models said about Ambassador
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“Avoid enterprise tools like Influitive/Ambassador/PartnerStack” Muse Glimmer 30B · negative prompt · hard negative
“enterprise tools that can be too pricey for small teams” Llama 4 Maverick · negative prompt · soft negative
“For most 100-500 person B2B SaaS teams starting out, Ambassador gives the best balance of advocacy and referral capabilities with enterprise-grade attribution” Muse Glimmer 30B · paraphrase prompt · first choice
“Start with Ambassador if you want to quickly scale referrals across customers, partners, and affiliates with minimal operational overhead.” Qwen 3.7 Flash · paraphrase 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.