Loopio vs Proposify
Twelve of fourteen models named Loopio first on the direct prompt; zero named Proposify. Loopio was named by fourteen of the fourteen models and Proposify by thirteen and Loopio carries 64 labels and Proposify 27, so the shares are not directly comparable.
Named in three categories this edition.
Named in five 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 RFP response software page.
By framing
LoopioFirst choices, of fourteen modelsProposify
Across every category in the November 2026 Edition, Loopio and Proposify were named in the same answer 100 times, of the 273 answers naming Loopio and the 290 naming Proposify. In those answers Proposify took the first choice one time and Loopio nineteen.
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 4.20 | | | | | | |
| 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 | | | | | |
Loopio Proposify first choice named as an alternative argued againstblank: not namedEach cell is one answer, Loopio on the left and Proposify on the right.The direct prompt
Loopio first, Proposify an alternative
12 of 14 modelsProposify 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
1 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
What the models said about Loopio
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“What to Avoid ... Loopio ... often require custom quotes typically ranging from $15,000 to $25,000+ per year” Qwen 3.7 Flash · budget prompt · hard negative
“Avoid (Unless Budget Allows Later): Loopio | Requires a custom quote; ... still enterprise-oriented.” Kimi K2 · budget prompt · hard negative
“Avoid enterprise tools like Responsive (formerly RFPIO), Loopio, or full AutoRFP.ai” Grok 4.20 · budget prompt · hard negative
“Loopio — Often the top-rated for mid-market. Excellent content library, strong AI suggestions, clean collaboration, great Salesforce integration.” Grok 4.20 · scale prompt · first choice
“Prefer specialized RFP response platforms ... (e.g., Responsive/formerly RFPIO, Loopio, or alternatives like RocketDocs, Tribble, or AutoRFP.ai)” Grok 4.20 · negative prompt · first choice
“the two names that consistently top the lists for RFP response / Strategic Response Management are Responsive, formerly RFPIO, and Loopio” Muse Glimmer 30B · direct prompt · first choice
What the models said about Proposify
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Tools like Proposify and PandaDoc are primarily designed for general proposal creation and lack the specialized features required for RFP response management” Muse Glimmer 30B · negative prompt · hard negative
“These are primarily sales proposal tools and are not designed for the specific workflows and compliance needs of RFP responses.” Mistral Small · negative prompt · hard negative
“1. Proposify (Most Popular for Mid-Market)” Claude Haiku 4.5 · 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.