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Proposal software · September 2026 Edition

PandaDoc vs Proposable

Nine of twelve models named PandaDoc first on the direct prompt; zero named Proposable. PandaDoc was named by twelve of the twelve models and Proposable by eight and PandaDoc carries 58 labels and Proposable 10, so the shares are not directly comparable.

PandaDoc

endorsed leader

South San Francisco, United States, founded 2013. Named in five categories this edition.

Proposable

accepted challenger

Named in one category this edition.

First-choice share49%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate17%10%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#8A position in a field of 10; printed, not drawn.
Labels5810A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, PandaDoc reading right to left. Rank and label count are printed, not drawn.Proposify was named alongside these two in eleven of the twelve direct answers. PandaDoc vs Proposify · PandaDoc vs Qwilr · PandaDoc vs Better Proposals

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.
PandaDocFirst choices, of twelve modelsProposable
Direct90
Paraphrase100
Comparative110
Budget-constrained312 against PandaDoc
Scale-constrained001 against PandaDoc
Negative007 against PandaDoc · 1 against Proposable
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.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where PandaDoc and Proposable stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
PandaDoc Proposable first choice named as an alternative argued againstblank: not namedEach cell is one answer, PandaDoc on the left and Proposable on the right.

The direct prompt

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

PandaDoc first, Proposable an alternative

9 of 12 modelsProposable was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5PandaDoc alternatives: ClientPoint, DealHub, Proposable
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
Kimi K2PandaDoc, Qwilr alternatives: Proposify
MiniMax M2.5PandaDoc alternatives: Proposify, Qwilr

Neither was the first choice, one was named

2 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Llama 4 MaverickProposify alternatives: Loopio, PandaDoc, Qwilr
GLM 4.7 FlashXProposify, Qwilr alternatives: PandaDoc

Neither was named

1 of 12 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniProposify alternatives: Better Proposals, Quoter

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
PandaDoc leads by forty-nine points.
PandaDoc49%#1 of 10
Proposable0%#8 of 10
The full small business standing →
Mid-marketThe figures above
PandaDoc leads by forty-seven points.
PandaDoc49%#1 of 10
Proposable2%#8 of 10
The full mid-market standing →
Enterprise
PandaDoc leads by twenty-three points.
PandaDoc23%#1 of 10
Proposable0%#– of 10
The full enterprise standing →

What the models said about PandaDoc

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

“Avoid: Tools like PandaDoc for government contracts, RFPs, or complex enterprise bids ... PandaDoc - Good for general sales proposals” GLM 4.7 FlashX · negative prompt · soft negative
“PandaDoc is currently the industry standard for mid-market businesses because it strikes the best balance between ease of use, functionality, and price.” Qwen 3.7 Flash · direct prompt · first choice
“Best for mid-market sales teams needing an all-in-one document workflow, it's the most widely adopted proposal AI-powered sales platform in B2B” Claude Haiku 4.5 · direct prompt · first choice
“Pick PandaDoc if you need one platform for proposals *and* contracts, NDAs, quotes, and other documents, with deep CRM integration.” DeepSeek V4 Flash · comparative prompt · first choice

What the models said about Proposable

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

“Proposable — Lightweight but Problematic” Kimi K2 · negative prompt · soft negative
“Proposable if you need CRM integrations and analytics on a tight budget” MiniMax M2.5 · budget prompt · first choice
“A balanced proposal tool for mid-market teams wanting more than basic features but less than enterprise-level complexity” Claude Haiku 4.5 · direct prompt · alternative
“A simple, lightweight option with a solid template library, built-in e-signatures” Gemini 3.5 Flash · budget prompt · alternative
Also compared

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.