Qwilr vs Loopio
Two of twelve models named Qwilr first on the direct prompt; zero named Loopio. Qwilr was named by twelve of the twelve models and Loopio by nine and Qwilr carries 44 labels and Loopio 18, so the shares are not directly comparable.
Redfern, Australia, founded 2014. Named in three categories this edition.
Named in one category 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 proposal software page.
By framing
QwilrFirst choices, of twelve modelsLoopio
Across every category in the September 2026 Edition, Qwilr and Loopio were named in the same answer forty-four times, of the 124 answers naming Qwilr and the 62 naming Loopio. In those answers Loopio took the first choice eight times and Qwilr three.
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 | | |
Qwilr Loopio first choice named as an alternative argued againstblank: not namedEach cell is one answer, Qwilr on the left and Loopio on the right.The direct prompt
Qwilr first, Loopio not the choice
2 of 12 modelsLoopio was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
6 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
4 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
Loopio4%#6 of 10Qwilr0%#10 of 10What the models said about Qwilr
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of six in this category shown.
“Tools like Qwilr are incredibly modern... However, they require caution depending on your industry.” Gemini 3.5 Flash · negative prompt · soft negative
“Limited template library; fewer automation options; limited integrations compared to competitors” Kimi K2 · negative prompt · soft negative
“Be cautious of general-purpose platforms like Proposify, Qwilr, and PandaDoc” Llama 4 Maverick · negative prompt · soft negative
“For most mid-market B2B companies, PandaDoc or Qwilr offer the best balance of features, pricing, and scalability.” Kimi K2 · direct prompt · first choice
“Proposify or Qwilr are the strongest starting points” GLM 4.7 FlashX · direct prompt · first choice
“Qwilr or GetAccept if a WOW-factor buyer experience is central to your sales motion” DeepSeek V4 Flash · paraphrase prompt · alternative
What the models said about Loopio
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Choose Loopio only if RFPs are central to your sales process — it's overkill for standard proposals.” Kimi K2 · direct prompt · soft negative
“notoriously expensive ($$$) and require weeks of training and configuration” Qwen 3.7 Flash · negative prompt · soft negative
“Loopio is often recommended for mid-market teams (50–300 RFPs/year), priced around $25K–$75K/year” DeepSeek V4 Flash · scale prompt · first choice
“invest in Enterprise Response Management systems like Responsive (formerly RFPIO) or Loopio” Gemini 3.5 Flash · negative prompt · alternative
“If you answer security questionnaires or complex RFPs, go with SiftHub or Loopio.” Gemini 3.5 Flash · comparative prompt · alternative
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.