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Index › Treasury and cash › Cash flow forecasting › Float vs Chaser
Cash flow forecasting · October 2026 Edition

Float vs Chaser

Zero of fourteen models named Float first on the direct prompt; three named Chaser. Float was named by thirteen of the fourteen models and Chaser by nine and Float carries 36 labels and Chaser 14, so the shares are not directly comparable.

Float

accepted challenger

Named in five categories this edition.

Chaser

accepted challenger

Named in seven categories this edition.

First-choice share8%8%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate22%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#4A position in a field of 15; printed, not drawn.
Labels3614A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Float reading right to left. Rank and label count are printed, not drawn.Cube was named alongside these two in nine of the fourteen direct answers. Agicap vs Float · Agicap vs Chaser · Float vs Cash Flow Frog

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 flow forecasting page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
FloatFirst choices, of fourteen modelsChaser
Direct03
Paraphrase01
Comparative20
Budget-constrained303 against Float
Scale-constrained101 against Float
Negative004 against Float
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 Float and Chaser 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
Float Chaser first choice named as an alternative argued againstblank: not namedEach cell is one answer, Float on the left and Chaser on the right.

The direct prompt

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

Chaser first, Float an alternative

3 of 14 modelsFloat was named in the answer but not as the choice, or not at all.
Grok 4.1 FastChaser alternatives: Drivetrain, Float, Jirav
Mistral SmallChaser, Cube alternatives: Prophix One, Tesorio
Kimi K2Chaser alternatives: Abacum, Cube, Fathom, Upflow

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 4.5Upflow alternatives: Abacum, Chaser, Cube, Tesorio
Perplexity SonarAgicap alternatives: Chaser, Cube, Upflow
DeepSeek V4 FlashTesorio alternatives: Agicap, Chaser, Float, Vena
Qwen 3.7 FlashAgicap alternatives: Chaser, Cube, Tesorio
MiniMax M2.5Upflow alternatives: Chaser, Drivetrain, Vena
Muse Glimmer 30BTesorio, Upflow alternatives: Abacum, Float, Planful

Neither was named

5 of 14 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniAgicap alternatives: Datarails, Kyriba, Vena
Gemini 3.5 FlashTesorio alternatives: Agicap, Centime, Cube, Nilus, Trovata
Llama 4 MaverickCube
GLM 4.7 FlashXCube alternatives: Abacum, HighRadius
GPT-6 LunaCentime alternatives: Cube, Jirav, Planful, 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 thirty-one points.
Float35%#1 of 12
Chaser4%#5 of 12
The full small business standing →
Mid-marketThe figures above
Level: the same share of first choices.
Float8%#2 of 15
Chaser8%#4 of 15
The full mid-market standing →
Enterprise
Float leads by two points.
Float2%#8 of 12
Chaser0%#– of 12
The full enterprise standing →

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.

“products such as Float, Helm, Dryrun, and Cash Flow Frog are described as good within a narrow scope, but they do not produce a full P&L or balance-sheet forecast” Perplexity Sonar · negative prompt · soft negative
“Float is risky if you are not on Xero/QuickBooks/FreeAgent, need heavy custom reporting, have strong seasonality..."Absolutely hopeless for seasonal business"” Muse Glimmer 30B · negative prompt · soft negative
“Poor seasonal business support | Float (limited to recent 3 months or manual input...) | Seasonal SMBs—users report it's "hopeless" without custom fixes.” Grok 4.1 Fast · negative prompt · soft negative
“If you are a small business using Xero/QuickBooks: Start with Float. It is the most intuitive and affordable way to stop guessing your cash runway.” GLM 4.7 FlashX · comparative prompt · first choice
“Float is the go-to cashflow forecasting pick for finance teams that need rolling views driven by invoices, bills, and bank activity” Muse Glimmer 30B · comparative prompt · first choice
“Float is frequently recommended as one of the best cash flow forecasting software options” Mistral Small · budget prompt · first choice

What the models said about Chaser

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

“I'd recommend Chaser as a top cash management tool. It's specifically built for mid-market B2B finance teams” Mistral Small · paraphrase 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.