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Index › Accounting and close › Account reconciliation › FloQast vs ReconArt
Account reconciliation · October 2026 Edition

FloQast vs ReconArt

Eight of fourteen models named FloQast first on the direct prompt; zero named ReconArt. FloQast was named by fourteen of the fourteen models and ReconArt by ten and FloQast carries 43 labels and ReconArt 16, so the shares are not directly comparable.

FloQast

endorsed leader

Named in ten categories this edition.

ReconArt

accepted challenger

Named in one category this edition.

First-choice share33%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate9%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#8A position in a field of 12; printed, not drawn.
Labels4316A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, FloQast reading right to left. Rank and label count are printed, not drawn.Trintech Adra was named alongside these two in twelve of the fourteen direct answers. FloQast vs Trintech Adra · FloQast vs Numeric · FloQast vs Zoho Books

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 account reconciliation page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
FloQastFirst choices, of fourteen modelsReconArt
Direct80
Paraphrase81
Comparative21
Budget-constrained002 against FloQast
Scale-constrained10
Negative002 against FloQast
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 FloQast and ReconArt 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
FloQast ReconArt first choice named as an alternative argued againstblank: not namedEach cell is one answer, FloQast on the left and ReconArt on the right.

The direct prompt

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

FloQast first, ReconArt not the choice

8 of 14 modelsReconArt was named in the answer but not as the choice, or not at all.
Gemini 3.5 FlashFloQast alternatives: BlackLine, Numeric, Trintech Adra, ZoneReconcile
Grok 4.1 FastFloQast alternatives: Numeric, Trintech Adra
DeepSeek V4 FlashFloQast alternatives: Numeric, Sage Intacct, Trintech Adra
Qwen 3.7 FlashFloQast, Numeric alternatives: Oracle NetSuite, Sage Intacct, Trintech Adra
Kimi K2FloQast, Numeric alternatives: Sage Intacct, Trintech Adra
GLM 4.7 FlashXFloQast, Trintech alternatives: BlackLine, HighRadius, Prophix One
GPT-6 LunaFloQast alternatives: Numeric, Trintech Adra
Muse Glimmer 30BFloQast alternatives: Sage Intacct, Trintech Adra

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.
Claude Haiku 4.5Trintech Adra alternatives: FloQast, Numeric, Prophix One
GPT-5.4 miniBlackLine alternatives: FloQast, Trintech Adra
Perplexity SonarTrintech Adra alternatives: Bluecopa, DOKKA, Numeric, Oracle Fusion Cloud EPM, Prophix One, ReconArt
Mistral SmallHighRadius alternatives: BlackLine, FloQast, Trintech

Neither was named

2 of 14 modelsThe answer made no first choice from these two in this category.
Llama 4 MaverickTrintech Adra alternatives: Oracle NetSuite, Ramp, Sage Intacct
MiniMax M2.5Trintech Adra, Xenett alternatives: Numeric, Sage Intacct, ZoneReconcile

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
FloQast leads by four points.
FloQast4%#6 of 10
ReconArt0%#9 of 10
The full small business standing →
Mid-marketThe figures above
FloQast leads by thirty-one points.
FloQast33%#1 of 12
ReconArt2%#8 of 12
The full mid-market standing →
Enterprise
FloQast leads by six points.
FloQast6%#4 of 10
ReconArt0%#10 of 10
The full enterprise standing →

What the models said about FloQast

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

“Some of the tools mentioned include FloQast, BlackLine, and AutoRek, which can be effective but may require additional time to address discrepancies.” Llama 4 Maverick · negative prompt · soft negative
“Excellent for collaboration, close-management, and balance sheet substantiation. It integrates deeply with Excel, making adoption incredibly easy” Gemini 3.5 Flash · scale prompt · first choice
“Most mid-market B2B companies find that FloQast or Trintech Adra provide the best balance of features, ease of use, and value for money.” GLM 4.7 FlashX · direct prompt · first choice

What the models said about ReconArt

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

“If you need specialized, high-volume automation: Start with ReconArt at ~$300/month.” GLM 4.7 FlashX · paraphrase prompt · first choice
“For large, complex, or high-volume reconciliations: ReconArt or BlackLine.” Mistral Small · comparative prompt · first choice
“a solid choice for medium to large businesses that need stronger reconciliation specialization” Perplexity Sonar · paraphrase prompt · alternative
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.