GTM AI Index
September 2026 Edition, expanded tier · The six flagship models on every category, as published September 9, 2026. The public index runs on the standard tier; this record is what the Expanded Edition Pack delivers on one category. The category page →
Index Revenue operations September 2026 Edition

Proposal software

Asked as “proposal software”, and as “sales proposal and quote document tool”, on behalf of a mid-market B2B software company. 45 first choices recorded across the direct, paraphrase, budget and scale prompts, six models each.
Standing
Clear leader
49% of first choices, clear leader.

01The standing

Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all six models, for a mid-market B2B software company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrant
01PandaDoc49%17%58endorsed leader
02Proposify13%17%54accepted challenger
03Qwilr4%7%44accepted challenger
04Better Proposals2%15%34accepted challenger
05DealHub2%6%18accepted challenger
06Loopio2%11%18accepted challenger
07Bonsai2%9%11accepted challenger
08Proposable2%10%10accepted challenger
Show the two products at 0%, ordered by negative rate
09GetAccept0%12%25accepted challenger
10Responsive0%6%16accepted challenger
Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its vendor page.

Recommended versus criticized

Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.

Criticized challengerCriticized default
Negative label rate →
01
02
03
04
05
06
07
08
09
10
Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative60%
Key
01PandaDoc49%
02Proposify13%
03Qwilr4%
04Better Proposals2%
05DealHub2%
06Loopio2%
07Bonsai2%
08Proposable2%
09GetAccept0%
10Responsive0%

02What they warned about

A high negative share on a product with few labels is a warning. A low share on a product with many labels is salience, not sentiment.
PandaDoc
17%
10 of 58 labels negative · 9 of 6 models
“**Avoid:** Tools like **PandaDoc** for government contracts, RFPs, or complex enterprise bids ... **PandaDoc** - Good for general sales proposals” GLM 4.7 FlashX, negative prompt
Proposify
17%
9 of 54 labels negative · 7 of 6 models · 3 hard negative
“Proposify — Multiple Critical Issues ... Proposify and SAP Ariba have the most severe and well-documented problems.” Kimi K2, negative prompt
Better Proposals
15%
5 of 34 labels negative · 5 of 6 models · 2 hard negative
“**Better Proposals** — Highest Concern ... Vendor lock-in / data hostage ... Fake/misleading integrations” DeepSeek V4 Flash, negative prompt
Qwilr
7%
3 of 44 labels negative · 3 of 6 models
“Tools like **Qwilr** are incredibly modern... However, they require caution depending on your industry.” Gemini 3.5 Flash, negative prompt

03What they cite

Citations exist only for the models that return a source list: four of the six in this edition.

Sites the answers cite

20 of 72 answers in this category came back with a source list, from 4 of 6 models: citations where the model returns them, or the search results it consulted. 6 of those lists are Google grounding redirects that name no site and are left out of the counts. 175 links across 75 sites, every framing counted. Ranked by the number of answers carrying the site or page. The other 2 models searched through a gateway that returned no source list on this edition.

vendor site · Qwilr10 answers · 15 citations · 3 models
vendor site · PandaDoc8 answers · 9 citations · 3 models
vendor site · AutogenAI6 answers · 8 citations · 2 models
vendor site · Better Proposals6 answers · 6 citations · 3 models
vendor site · GetAccept6 answers · 6 citations · 2 models
vendor site · Proposify5 answers · 6 citations · 2 models
5 answers · 6 citations · 2 models
vendor site · Lindy5 answers · 5 citations · 2 models
vendor site · Oneflow4 answers · 4 citations · 2 models
vendor site · Zapier4 answers · 4 citations · 1 model
3 answers · 6 citations · 1 model
3 answers · 4 citations · 2 models

Pages the answers cite

The ten pages named in the most answers, by full address. A page here is one the models returned with a recommendation, not one the index endorses.

04How they answered

Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.
ModelDirect“What is the best proposal software for a mid-market B2B company?”Paraphrase“Which sales proposal and quote document tool would you recommend to a mid-sized B2B company?”Comparative“What are the top proposal tools and how do they differ?”Budget-constrained“What is the best proposal software for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating a proposal software. What should we look at?”Negative“Which proposal tools should I avoid or be cautious about?”
Claude Opus 5no first choiceno first choiceno first choiceno first choiceno first choicenothing named
Claude Opus 4.8no first choiceno first choiceno first choiceno first choiceno first choicenothing named
GPT-6 Astrano first choiceno first choiceno first choiceno first choiceno first choicenothing named
GPT-5.6 Solno first choiceno first choiceno first choiceno first choiceno first choicenothing named
Gemini 3.1 Prono first choiceno first choiceno first choiceno first choiceno first choicenothing named
Perplexity Sonar Prono first choiceno first choiceno first choiceno first choiceno first choicenothing named
Bold is the first choiceAlternatives are counted; the count opens them.What the answer argued against

05The record

One row per call: the version string exactly as returned, whether the model searched, sources cited, and latency. Full answer text is in the free responses file. Download the record
Zero rows: every prompt, every model, every answer.
PromptModelVersion stringTime (UTC)SearchedSourcesLatency

Normalization in this category

Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.

Category-scoped readings
None. Every name in this category resolved on its own.
Unresolved, counted raw
Arpixa
Bloom
Bookipi Proposal AI
DocuSeal
Dropbox Paper
FreelanceDesk
Google Docs / Slides
HubSpot Commerce Hub & Quotes
Kintone
LibreOffice Writer
PandaDoc Free eSign
Piktochart
Proposal.biz
Ramp CPQ
SoloTools
talentproposals.online
talentproposals.online / beforewetalk.com
Discontinued, still offered
No shut-down product was recommended here.
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