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Proposal software · September 2026 Edition

Proposify vs Qwilr

Three of twelve models named Proposify first on the direct prompt; two named Qwilr. Both were named by all twelve models and Proposify carries 54 labels and Qwilr 44, so the shares are not directly comparable.

Proposify

accepted challenger

Named in three categories this edition.

Qwilr

accepted challenger

Redfern, Australia, founded 2014. Named in three categories this edition.

First-choice share13%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate17%7%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#3A position in a field of 10; printed, not drawn.
Labels5444A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Proposify reading right to left. Rank and label count are printed, not drawn.PandaDoc was named alongside these two in eleven of the twelve direct answers. PandaDoc vs Proposify · PandaDoc vs Qwilr

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

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
ProposifyFirst choices, of twelve modelsQwilr
Direct32
Paraphrase20
Comparative00
Budget-constrained102 against Proposify
Scale-constrained001 against Proposify
Negative006 against Proposify · 3 against Qwilr
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

The direct prompt

The plain question, one answer per model, grouped by where Proposify and Qwilr stood in it.

Both were the first choice

1 of 12 modelsThe answer named them together, and the judge labeled each a first choice.
GLM 4.7 FlashXProposify, Qwilr alternatives: PandaDoc

Proposify first, Qwilr an alternative

2 of 12 modelsQwilr was named in the answer but not as the choice, or not at all.
GPT-5.4 miniProposify alternatives: Better Proposals, Quoter
Llama 4 MaverickProposify alternatives: Loopio, PandaDoc, Qwilr

Qwilr first, Proposify an alternative

1 of 12 modelsProposify was named in the answer but not as the choice, or not at all.
Kimi K2PandaDoc, Qwilr alternatives: Proposify

Neither was the first choice, one was named

7 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashPandaDoc alternatives: GetAccept, Loopio, Proposify, Qwilr, SiftHub, Trumpet
Perplexity SonarPandaDoc alternatives: Proposify
Grok 4.1 FastPandaDoc alternatives: GetAccept, Proposify
Mistral SmallPandaDoc alternatives: GetAccept, Proposify, Qwilr
DeepSeek V4 FlashPandaDoc alternatives: DealHub, Loopio, Proposify, Qwilr
Qwen 3.7 FlashPandaDoc alternatives: Loopio, Proposify, Qwilr
MiniMax M2.5PandaDoc alternatives: Proposify, Qwilr

Neither was named

1 of 12 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5PandaDoc alternatives: ClientPoint, DealHub, Proposable

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Proposify leads by six points.
Proposify9%#3 of 10
Qwilr2%#7 of 10
The full small business standing →
Mid-marketThe figures above
Proposify leads by nine points.
Proposify13%#2 of 10
Qwilr4%#3 of 10
The full mid-market standing →
Enterprise
Proposify leads by two points.
Proposify2%#7 of 10
Qwilr0%#10 of 10
The full enterprise standing →

What the models said about Proposify

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.

“Proposify — Multiple Critical Issues ... Proposify and SAP Ariba have the most severe and well-documented problems.” Kimi K2 · negative prompt · hard negative
“Frequent reports of poor customer service ... Trustpilot rating ~2.3/5; some call it "buggy and unusable."” Grok 4.1 Fast · negative prompt · hard negative
“Predatory billing practices - Charging after cancellation, making refunds nearly impossible” Mistral Small · negative prompt · hard negative
“my default pick would be Proposify if you want a balanced mix of proposal creation, approvals, tracking, and CRM-friendly workflow” GPT-5.4 mini · direct prompt · first choice
“For most mid-market B2B companies, Proposify or Qwilr are the strongest starting points” GLM 4.7 FlashX · direct prompt · first choice
“the best sales proposal and quote document tool for a mid-sized B2B company would be Proposify” Llama 4 Maverick · paraphrase prompt · first choice

What 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
Also compared

Comparisons are drawn for the top three products in each category. The output is the models' output; nothing here is a recommendation by the index.