Finance AI Index
Index Receivables and billing Cash application › Versapay vs Lunos
Cash application · September 2026 Edition

Versapay vs Lunos

One of twelve models named Versapay first on the direct prompt; zero named Lunos. Versapay was named by twelve of the twelve models and Lunos by six and Versapay carries 28 labels and Lunos 11, so the shares are not directly comparable.

Versapay

accepted challenger

Named in five categories this edition.

Lunos

accepted challenger

Named in two categories this edition.

First-choice share11%7%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate11%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#4A position in a field of 10; printed, not drawn.
Labels2811A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Versapay reading right to left. Rank and label count are printed, not drawn.HighRadius was named alongside these two in seven of the twelve direct answers. HighRadius vs Versapay · HighRadius vs Lunos · Versapay vs Upflow

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 cash application page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
VersapayFirst choices, of twelve modelsLunos
Direct10
Paraphrase41
Comparative00
Budget-constrained021 against Versapay
Scale-constrained00
Negative002 against Versapay
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 Versapay and Lunos 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
Versapay Lunos first choice named as an alternative argued againstblank: not namedEach cell is one answer, Versapay on the left and Lunos on the right.

The direct prompt

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

Versapay first, Lunos not the choice

1 of 12 modelsLunos was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Versapay alternatives: Billtrust, Paystand, Quadient Accounts Receivable, Upflow

Neither was the first choice, one was named

5 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashQuadient Accounts Receivable alternatives: Stuut, Tesorio, Upflow, Versapay
DeepSeek V4 FlashEsker, HighRadius alternatives: Gaviti, Monk, Versapay
Llama 4 MaverickUpflow alternatives: Bluecopa, Cashbook, Chaser, Lunos
Kimi K2HighRadius alternatives: Billtrust, Chaser, Versapay
GLM 4.7 FlashXEsker, Kolleno alternatives: Chaser, Gaviti, Monk, Quadient Accounts Receivable, Serrala, Stuut, Transparency / Transformance, Versapay

Neither was named

6 of 12 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniHighRadius alternatives: BlackLine Cash Application, Sidetrade
Perplexity SonarHighRadius alternatives: Billtrust, Kolleno, Upflow
Grok 4.1 FastEsker alternatives: Billtrust, Gaviti, HighRadius, Quadient Accounts Receivable
Mistral SmallUpflow alternatives: Bluecopa, Cashbook, Chaser
Qwen 3.7 FlashHighRadius alternatives: AtoBi, BlackLine Cash Application, Trintech
MiniMax M2.5HighRadius alternatives: Monk

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
Lunos leads by two points.
Lunos4%#5 of 10
Versapay2%#6 of 10
The full small business standing →
Mid-marketThe figures above
The order flips: Versapay leads at mid-market.
Versapay11%#2 of 10
Lunos7%#4 of 10
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Versapay0%#5 of 7
Lunos0%#– of 7
The full enterprise standing →

What the models said about Versapay

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

“What to avoid on a limited budget - Versapay ... Definitely not budget-friendly.” DeepSeek V4 Flash · budget prompt · hard negative
“Reviews highlight limited backend automation, lack of native credit or deduction management, and weak integration capabilities” Qwen 3.7 Flash · negative prompt · soft negative
“Caution Level: Medium (for ERP integration and reporting)... integration is a beast... rules-based logic can misfire” DeepSeek V4 Flash · negative prompt · soft negative
“Best for mid-market B2B companies wanting cash application bundled with payment rails and a collaborative customer portal” Claude Haiku 4.5 · direct prompt · first choice
“my default recommendation would be Versapay if your main goal is payment matching / cash application automation” GPT-5.4 mini · paraphrase prompt · first choice

What the models said about Lunos

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

“Best Pay-As-You-Go Solution: Lunos ... "I want zero monthly risk." | Lunos” Qwen 3.7 Flash · budget prompt · first choice
“Lunos (backed by GoCardless/General Catalyst) is the strongest fit for mid-market” DeepSeek V4 Flash · paraphrase prompt · first choice
“Lunos – Best overall for a free/very low-cost start” DeepSeek V4 Flash · 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.