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Index Treasury and cash Cash flow forecasting › Agicap vs Float
Cash flow forecasting · September 2026 Edition

Agicap vs Float

One of twelve models named Agicap first on the direct prompt; zero named Float. Agicap was named by eleven of the twelve models and Float by twelve and Agicap carries 24 labels and Float 27, so the shares are not directly comparable.

Agicap

accepted challenger

Named in three categories this edition.

Float

accepted challenger

Named in four categories this edition.

First-choice share18%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate4%22%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#5A position in a field of 14; printed, not drawn.
Labels2427A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Agicap reading right to left. Rank and label count are printed, not drawn.Cube was named alongside these two in nine of the twelve direct answers. Agicap vs Tesorio · Agicap vs Chaser · Agicap vs Cube

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 flow forecasting page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
AgicapFirst choices, of twelve modelsFloat
Direct10
Paraphrase70
Comparative03
Budget-constrained023 against Float
Scale-constrained10
Negative011 against Agicap · 3 against Float
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.

Across every category in the September 2026 Edition, Agicap and Float were named in the same answer thirty-three times, of the 94 answers naming Agicap and the 126 naming Float. In those answers Float took the first choice eleven times and Agicap five.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Agicap and Float 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
Agicap Float first choice named as an alternative argued againstblank: not namedEach cell is one answer, Agicap on the left and Float on the right.

The direct prompt

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

Agicap first, Float an alternative

1 of 12 modelsFloat was named in the answer but not as the choice, or not at all.
GPT-5.4 miniAgicap alternatives: Cube, Float, Tesorio

Neither was the first choice, one was named

4 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashCentime, Tesorio alternatives: Agicap, Cube, Mosaic, Trovata
Llama 4 MaverickCube alternatives: Abacum, Agicap, Chaser, GTreasury
Kimi K2Tesorio alternatives: Agicap, Chaser, Cube, Float
GLM 4.7 FlashXChaser alternatives: Abacum, Drivetrain, Float, Futrli, Upflow

Neither was named

7 of 12 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Tesorio alternatives: Billtrust, Cube, Drivetrain
Perplexity SonarAbacum, Drivetrain alternatives: Chaser, Cube, Vena
Grok 4.1 FastChaser, Drivetrain alternatives: Abacum, Upflow
Mistral SmallCube alternatives: Abacum, Chaser
DeepSeek V4 FlashAbacum alternatives: Centime, Chaser, Cube, Drivetrain
Qwen 3.7 FlashChaser alternatives: Abacum, Phocas Software, Vena
MiniMax M2.5Chaser alternatives: Cube, Tesorio

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
Float leads by twenty-nine points.
Float31%#1 of 8
Agicap2%#5 of 8
The full small business standing →
Mid-marketThe figures above
The order flips: Agicap leads at mid-market.
Agicap18%#1 of 14
Float4%#5 of 14
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Agicap0%#– of 10
Float0%#10 of 10
The full enterprise standing →

What the models said about Agicap

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

“Caution level: High — but not a flat "avoid"” DeepSeek V4 Flash · negative prompt · soft negative
“For most mid‑sized B2B companies ... Agicap is the most frequently recommended option in recent reviews.” MiniMax M2.5 · paraphrase prompt · first choice

What the models said about Float

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

“Avoid if: Your business has peaks/troughs (e.g., retail, tourism). Users call it "hopeless" without heavy manual tweaks.” Grok 4.1 Fast · negative prompt · hard negative
“Float has been criticized for being "absolutely hopeless for seasonal business as it can only predict income based on the previous 3 months or manual guessing"a” Kimi K2 · negative prompt · soft negative
“Some tools, like Float or Fluidly, may not be suitable for rapidly growing or contracting businesses” Mistral Small · negative prompt · soft negative
“If I had to pick one best option for most budget-conscious small businesses, it would be Float” Perplexity Sonar · budget prompt · first choice
“If you want live operational cash visibility, look at Float or Dryrun.” GPT-5.4 mini · comparative prompt · first choice
“the best value is usually Float if you use Xero or QuickBooks Online” GPT-5.4 mini · 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.