Finance AI Index
Index Vendors › Cadel · September 2026 Edition
1 category · Named, not ranked

Cadel

3Judge labels
1First choices
0Negative labels
3 of 12Models named it
1Category
September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.2, every buyer segment counted.
Standing
3 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. Cadel was named 3 times in Revenue recognition, where Maxio led with 15%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
In revenue recognition · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named Cadel for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
CategoryFunctionShareRankNegative rateLabelsQuadrant
Revenue recognitionAccounting and close2%17 of 770%3under 10 labels · led by Maxio at 15%

Movement

This is the first edition on this tier, so no move can be computed for Cadel yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated Cadel across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500000
GPT-5.4 mini00000
Gemini 3.5 Flash01001
Perplexity Sonar00000
Grok 4.1 Fast00000
Mistral Small00000
DeepSeek V4 Flash00000
Llama 4 Maverick10001
Qwen 3.7 Flash00000
Kimi K200000
GLM 4.7 FlashX00000
MiniMax M2.501001

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct0 labelsNone
Paraphrase3 labels1
Comparative0 labelsNone
Budget-constrained0 labelsNone
Scale-constrained0 labelsNone
Negative0 labelsNone
First choiceAlternativeMentionNegative3 labels in all, every segment counted; 1 of the 1 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“1. Cadel.ai - Automates the full ASC 606 workflow, including contract parsing, revenue scheduling, and journal entry posting.” Llama 4 Maverick · Revenue recognition · paraphrase prompt · first choice
“Cadel is designed specifically to automate ASC 606 for the US mid-market” Gemini 3.5 Flash · Revenue recognition · paraphrase prompt · alternative
“Cadel (Best for Contract-to-Journal Workflow)” MiniMax M2.5 · Revenue recognition · paraphrase prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

No model argued against it.

Named alongside

The products named in the same answers as Cadel, over the 3 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Cadel was named but was not.
ProductSame answerTook the first choice insteadHead to head
RightRev3 of 30Not in the top three
LedgerUp2 of 31Not in the top three
Sage Intacct2 of 30Not in the top three
Maxio1 of 31Not in the top three
Ordway1 of 31Not in the top three
Chargebee RevRec1 of 30Not in the top three
NetSuite Advanced Revenue Management1 of 30Not in the top three
NetSuite RevRec AI1 of 30Not in the top three
Oracle RevPro1 of 30Not in the top three
Tabs1 of 30Not in the top three
A head-to-head page exists where both products are in a category's top three. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named Cadel. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 2 of the 3 answers that named Cadel and are not a share of its labels.

Names read as Cadel

What the judge wrote, as written, with how often. The vendor table decides that these count as Cadel; a claim can dispute any of them.
Cadel.ai 1
Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Cadel's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Cadel, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at cadel.ai is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.