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RFP response software · November 2026 Edition

PandaDoc vs DeepRFP

Zero of fourteen models named PandaDoc first on the direct prompt; zero named DeepRFP. PandaDoc was named by fourteen of the fourteen models and DeepRFP by ten and PandaDoc carries 35 labels and DeepRFP 13, so the shares are not directly comparable.

PandaDoc

criticized challenger

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

DeepRFP

accepted challenger

Named in one category this edition.

First-choice share20%6%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate26%8%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#5A position in a field of 11; printed, not drawn.
Labels3513A 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.Loopio was named alongside these two in thirteen of the fourteen direct answers. Loopio vs PandaDoc · Loopio vs DeepRFP · PandaDoc vs Responsive

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
PandaDocFirst choices, of fourteen modelsDeepRFP
Direct00
Paraphrase110
Comparative003 against PandaDoc
Budget-constrained031 against DeepRFP
Scale-constrained001 against PandaDoc
Negative005 against PandaDoc
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

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

The direct prompt

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

Neither was the first choice, one was named

4 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Grok 4.20Loopio alternatives: PandaDoc, Proposify, Qwilr, Responsive
DeepSeek V4 FlashLoopio, Responsive alternatives: PandaDoc
Kimi K2Loopio, Responsive alternatives: PandaDoc
MiniMax M2.5Responsive alternatives: Loopio, PandaDoc

Neither was named

10 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Loopio, Steerlab alternatives: AutoRFP.ai, SiftHub
GPT-5.4 miniLoopio alternatives: Responsive, Upland Qvidian
Gemini 3.5 FlashLoopio alternatives: AutoRFP.ai, Inventive AI, QorusDocs, Responsive
Perplexity SonarLoopio alternatives: RequestFX, Responsive, Steerlab
Mistral SmallLoopio alternatives: Responsive, Upland Qvidian
Llama 4 Maverickno first choice
Qwen 3.7 FlashLoopio alternatives: CQ Core, Responsive, Upland Qvidian
GLM 4.7 FlashXLoopio, Responsive alternatives: Better Proposals, eBuyerAssist Platform
GPT-6 LunaLoopio alternatives: Responsive
Muse Glimmer 30BLoopio, Responsive alternatives: Upland Qvidian

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 nineteen points.
PandaDoc30%#1 of 12
DeepRFP11%#3 of 12
The full small business standing →
Mid-marketThe figures above
PandaDoc leads by fifteen points.
PandaDoc20%#2 of 11
DeepRFP6%#5 of 11
The full mid-market standing →
Enterprise
PandaDoc leads by four points.
PandaDoc4%#5 of 10
DeepRFP0%#– 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 four in this category shown.

“primarily sales proposal tools and are not designed for the specific workflows and compliance needs of RFP responses” Mistral Small · negative prompt · hard negative
“Proposify and PandaDoc are proposal tools rather than RFP tools and the difference matters.” Muse Glimmer 30B · negative prompt · hard negative
“Start with PandaDoc if you need a general sales proposal tool—it offers the best combination of functionality, integrations, and price for growing teams.” Kimi K2 · paraphrase prompt · first choice
“PandaDoc is the most frequently recommended default because of its mid-market G2 leadership, CRM integration depth and proposal-to-contract coverage.” Muse Glimmer 30B · paraphrase prompt · first choice

What the models said about DeepRFP

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

“another low-cost option mentioned for freelancers and very small teams, though I'd verify fit carefully before buying” GPT-5.4 mini · budget prompt · soft negative
“The best RFP response software for a company with a limited budget is DeepRFP, as it is a cheaper entry for small teams” Llama 4 Maverick · budget prompt · first choice
“Built specifically for small business owners and lean proposal teams who don't want complex enterprise setups” Gemini 3.5 Flash · budget prompt · first choice
“Tightest budget + need AI help: Start with DeepRFP's free trial.” Kimi K2 · budget prompt · first choice
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.