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Index › Receivables and billing › Accounts receivable auto › Zoho Books vs Quadient AR
Accounts receivable automation · October 2026 Edition

Zoho Books vs Quadient AR

Zero of fourteen models named Zoho Books first on the direct prompt; three named Quadient AR. Zoho Books was named by twelve of the fourteen models and Quadient AR by fourteen and Zoho Books carries 12 labels and Quadient AR 31, so the shares are not directly comparable.

Zoho Books

accepted challenger

Named in seventeen categories this edition.

Quadient AR

accepted challenger

Named in six categories this edition.

First-choice share13%8%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%13%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#5A position in a field of 12; printed, not drawn.
Labels1231A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Zoho Books reading right to left. Rank and label count are printed, not drawn.Versapay was named alongside these two in thirteen of the fourteen direct answers. Upflow vs Zoho Books · Upflow vs Quadient AR · Zoho Books vs Versapay

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 accounts receivable automation page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
Zoho BooksFirst choices, of fourteen modelsQuadient AR
Direct031 against Quadient AR
Paraphrase01
Comparative00
Budget-constrained701 against Quadient AR
Scale-constrained00
Negative002 against Quadient AR
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 Zoho Books and Quadient AR 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
GPT-6 Luna
Muse Glimmer 30B
Zoho Books Quadient AR first choice named as an alternative argued againstblank: not namedEach cell is one answer, Zoho Books on the left and Quadient AR on the right.

The direct prompt

The plain question, one answer per model, grouped by where Zoho Books and Quadient AR stood in it.

Quadient AR first, Zoho Books not the choice

3 of 14 modelsZoho Books was named in the answer but not as the choice, or not at all.
Gemini 3.5 FlashQuadient AR alternatives: Billtrust, Paystand, Tesorio, Versapay
Mistral SmallQuadient AR, Upflow alternatives: Sage Intacct, Versapay
Llama 4 MaverickQuadient AR, Versapay

Neither was the first choice, one was named

6 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5Monk alternatives: BILL Accounts Receivable, Invoiced, Quadient AR, Versapay
GPT-5.4 miniBilltrust alternatives: Gaviti, HighRadius, Quadient AR
Grok 4.1 FastBilltrust alternatives: Chaser, Gaviti, Quadient AR, Tesorio, Upflow, Versapay
DeepSeek V4 FlashUpflow alternatives: Billtrust, Chaser, Quadient AR, Versapay
Qwen 3.7 FlashUpflow alternatives: Quadient AR, Sage Intacct, Versapay
MiniMax M2.5Billtrust alternatives: Chaser, Gaviti, Quadient AR, Versapay

Neither was named

5 of 14 modelsThe answer made no first choice from these two in this category.
Perplexity SonarVersapay alternatives: Billtrust, HighRadius, Invoiced
Kimi K2HighRadius alternatives: Billtrust, Chaser, Gaviti, Upflow, Versapay
GLM 4.7 FlashXVersapay alternatives: Billtrust, Chaser, HighRadius, Upflow
GPT-6 LunaVersapay alternatives: Billtrust, HighRadius, Tesorio
Muse Glimmer 30BBilltrust, HighRadius alternatives: Chaser, Esker, Invoiced, Sidetrade, Tesorio, Upflow, Versapay

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
Zoho Books leads by thirteen points.
Zoho Books15%#3 of 13
Quadient AR2%#– of 13
The full small business standing →
Mid-marketThe figures above
Zoho Books leads by six points.
Zoho Books13%#2 of 12
Quadient AR8%#5 of 12
The full mid-market standing →
Enterprise
The order flips: Quadient AR leads at enterprise.
Quadient AR4%#– of 9
Zoho Books0%#– of 9
The full enterprise standing →

What the models said about Zoho Books

No label in this category carried a quote.

What the models said about Quadient AR

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

“Be cautious of platforms like Quadient AR (formerly YayPay) or older point solutions if they do not have deep, bidirectional, real-time sync” Gemini 3.5 Flash · negative prompt · soft negative
“Most vendors (including Quadient AR/YayPay, Billtrust, Gaviti, Sidetrade) publish no pricing at all” Kimi K2 · negative prompt · soft negative
“Rather than expensive, enterprise-grade platforms (like HighRadius or Quadient, which cost thousands)” Gemini 3.5 Flash · budget prompt · soft negative
“The best accounts receivable automation software for a mid-market B2B company is Quadient AR (by YayPay) or Versapay.” Llama 4 Maverick · direct prompt · first choice
“I'd recommend starting with Quadient AR (YayPay) since it's specifically designed for mid-market B2B collections” MiniMax M2.5 · paraphrase prompt · first choice
“Best Overall for Collections Optimization and Usability” Gemini 3.5 Flash · direct 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.