AI Indexes
Finance AI Index
Index › Accounting and close › Crypto accounting › Breezing vs SoftLedger
Crypto accounting · October 2026 Edition

Breezing vs SoftLedger

Zero of fourteen models named Breezing first on the direct prompt; zero named SoftLedger. Breezing was named by eleven of the fourteen models and SoftLedger by twelve and Breezing carries 19 labels and SoftLedger 13, so the shares are not directly comparable.

Breezing

accepted challenger

Named in one category this edition.

SoftLedger

accepted challenger

Named in two categories this edition.

First-choice share14%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%8%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#8A position in a field of 14; printed, not drawn.
Labels1913A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Breezing reading right to left. Rank and label count are printed, not drawn.Bitwave was named alongside these two in eleven of the fourteen direct answers. Bitwave vs Breezing · Bitwave vs SoftLedger · Cryptoworth vs Breezing

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 crypto accounting page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
BreezingFirst choices, of fourteen modelsSoftLedger
Direct001 against SoftLedger
Paraphrase32
Comparative00
Budget-constrained50
Scale-constrained00
Negative00
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.

Across every category in the October 2026 Edition, Breezing and SoftLedger were named in the same answer twenty-six times, of the 67 answers naming Breezing and the 62 naming SoftLedger. In those answers SoftLedger took the first choice three times and Breezing fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Breezing and SoftLedger 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
Breezing SoftLedger first choice named as an alternative argued againstblank: not namedEach cell is one answer, Breezing on the left and SoftLedger on the right.

The direct prompt

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

Neither was the first choice, one was named

2 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5Cryptoworth alternatives: Bitwave, Breezing, Cryptio
GPT-5.4 miniCryptio alternatives: Bitwave, SoftLedger

Neither was named

12 of 14 modelsThe answer made no first choice from these two in this category.
Gemini 3.5 FlashBitwave alternatives: Cryptio, Cryptoworth, TRES Finance
Perplexity SonarCryptio alternatives: Bitwave, CryptaCount, Ledgible, TRES Finance
Grok 4.1 FastBitwave alternatives: Cryptio, Cryptoworth
Mistral SmallCryptoworth alternatives: CryptaCount
DeepSeek V4 FlashBitwave alternatives: Cryptio, Cryptoworth, Ledgible
Llama 4 MaverickCryptaCount alternatives: Ledgible
Qwen 3.7 FlashCryptaCount alternatives: Bitwave
Kimi K2Cryptoworth, Ledgible alternatives: Bitwave, TRES Finance
GLM 4.7 FlashXBitwave alternatives: CoinTracker, Cryptio, Cryptoworth, Ledgible
MiniMax M2.5CryptaCount, Ledgible alternatives: Node40
GPT-6 LunaBitwave alternatives: Cryptio, Ledgible
Muse Glimmer 30BCryptoworth alternatives: Bitwave, Cryptio, Ledgible

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
Breezing leads by forty-two points.
Breezing47%#1 of 12
SoftLedger5%#4 of 12
The full small business standing →
Mid-marketThe figures above
Breezing leads by eleven points.
Breezing14%#3 of 14
SoftLedger4%#8 of 14
The full mid-market standing →
Enterprise
Breezing leads by two points.
Breezing6%#– of 11
SoftLedger4%#5 of 11
The full enterprise standing →

What the models said about Breezing

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

“I'd recommend Breezing, which targets small-to-midsize businesses with published pricing from $29 to $2,917 per month” Claude Haiku 4.5 · paraphrase prompt · first choice
“Breezing (Best Overall Budget Subledger) ... widely considered the most cost-effective, dedicated crypto subledger” Gemini 3.5 Flash · budget prompt · first choice
“For the most budget-conscious choice, Breezing or Koinly's free tier would be ideal starting points.” Claude Haiku 4.5 · budget prompt · first choice

What the models said about SoftLedger

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

“Full ledger replacement, but pricier (~$13k+/yr).” Grok 4.1 Fast · direct prompt · soft negative
“SoftLedger is the safer default recommendation because it appears better aligned to mid-market needs and pricing expectations” Perplexity Sonar · paraphrase prompt · first choice
“Bitwave or SoftLedger are the top recommendations” Mistral Small · paraphrase prompt · first choice
“SoftLedger is another option, starting at $45–$750 per month” Mistral Small · budget 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.