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Cash application · October 2026 Edition

Stuut vs Billtrust

Three of fourteen models named Stuut first on the direct prompt; one named Billtrust. Stuut was named by ten of the fourteen models and Billtrust by thirteen and Stuut carries 18 labels and Billtrust 42, so the shares are not directly comparable.

Stuut

accepted challenger

Named in three categories this edition.

Billtrust

criticized challenger

Named in nine categories this edition.

First-choice share6%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%29%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#7A position in a field of 14; printed, not drawn.
Labels1842A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Stuut reading right to left. Rank and label count are printed, not drawn.Versapay was named alongside these two in eight of the fourteen direct answers. Versapay vs Stuut · Versapay vs Billtrust · Upflow vs Stuut

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 cash application page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
StuutFirst choices, of fourteen modelsBilltrust
Direct311 against Billtrust
Paraphrase00
Comparative11
Budget-constrained003 against Billtrust
Scale-constrained001 against Billtrust
Negative007 against Billtrust
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 Stuut and Billtrust 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
Stuut Billtrust first choice named as an alternative argued againstblank: not namedEach cell is one answer, Stuut on the left and Billtrust on the right.

The direct prompt

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

Stuut first, Billtrust an alternative

3 of 14 modelsBilltrust was named in the answer but not as the choice, or not at all.
Mistral SmallStuut alternatives: Versapay
DeepSeek V4 FlashStuut alternatives: Billtrust, Monk, SINGOA, Versapay
Kimi K2Stuut alternatives: HighRadius, Quadient Accounts Receivable, Versapay

Billtrust first, Stuut not the choice

1 of 14 modelsStuut was named in the answer but not as the choice, or not at all.
GPT-6 LunaBilltrust alternatives: Esker, HighRadius

Neither was the first choice, one was named

3 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5HighRadius alternatives: Billtrust, Quadient Accounts Receivable, Versapay
Grok 4.1 Fastno first choice alternatives: Billtrust, HighRadius, Quadient Accounts Receivable, Stuut, Versapay
GLM 4.7 FlashXHighRadius alternatives: Billtrust, Esker, Serrala, Stuut, Versapay

Neither was named

7 of 14 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniBilltrust Unified AR alternatives: BlackLine Invoice-to-Cash, HighRadius Accounts Receivable Software
Gemini 3.5 FlashVersapay alternatives: Cashbook, HighRadius, Quadient Accounts Receivable, Upflow
Perplexity SonarHighRadius alternatives: Chaser, Rex, Upflow, Versapay
Llama 4 MaverickMonk alternatives: Cashbook, Gaviti, Upflow
Qwen 3.7 FlashGlid alternatives: BlackLine Cash Application, Tipalti, Workiva
MiniMax M2.5no first choice
Muse Glimmer 30Bno first choice alternatives: NetSuite’s built-in cash application module

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
Stuut leads by two points.
Stuut2%#– of 16
Billtrust0%#14 of 16
The full small business standing →
Mid-marketThe figures above
Stuut leads by four points.
Stuut6%#5 of 14
Billtrust2%#7 of 14
The full mid-market standing →
Enterprise
Stuut leads by six points.
Stuut11%#– of 8
Billtrust4%#2 of 8
The full enterprise standing →

What the models said about Stuut

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

“For most mid-market B2B companies, I'd recommend: 1. Stuut if you need fast implementation” Kimi K2 · direct prompt · first choice
“the AI-native platforms (Stuut, SINGOA, Monk) or Versapay often deliver the fastest ROI” DeepSeek V4 Flash · direct prompt · first choice
“If you need rapid deployment and high automation, consider Stuut or Emagia.” Mistral Small · comparative prompt · first choice

What the models said about Billtrust

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

“"Avoid for Your Criteria - Billtrust, Serrala, Esker, Versapay: Custom/transaction-based"” Muse Glimmer 30B · negative prompt · hard negative
“The biggest red flags are HighRadius and Billtrust for standard contract terms” DeepSeek V4 Flash · negative prompt · hard negative
“Recommended to negotiate carefully or avoid standard contracts due to high costs” Mistral Small · negative prompt · hard negative
“I'd start with Billtrust. Its cash-application product is specifically focused on bringing together payment and remittance data” GPT-6 Luna · 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.