AI accounting assistants · October 2026 Edition
Sage Intacct vs Vic.ai
Nine of fourteen models named Sage Intacct first on the direct prompt; two named Vic.ai. Sage Intacct was named by thirteen of the fourteen models and Vic.ai by thirteen and Sage Intacct carries 24 labels and Vic.ai 30, so the shares are not directly comparable.
Named in nineteen categories this edition.
Named in three categories this edition.
First-choice share22%9%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%7%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#4A position in a field of 17; printed, not drawn.
Labels2430A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Sage Intacct reading right to left. Rank and label count are printed, not drawn.Oracle NetSuite was named alongside these two in eight of the fourteen direct answers. Sage Intacct vs Rillet · Sage Intacct vs Zoho Books · Sage Intacct vs Wave
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 AI accounting assistants page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
Sage IntacctFirst choices, of fourteen modelsVic.ai
Direct92
Paraphrase02
Comparative01
Budget-constrained00
Scale-constrained10
Negative102 against Vic.ai
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 Sage Intacct and Vic.ai stood in it.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| 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 | | |
Sage Intacct Vic.ai first choice named as an alternative argued againstblank: not namedEach cell is one answer, Sage Intacct on the left and Vic.ai on the right.The direct prompt
The plain question, one answer per model, grouped by where Sage Intacct and Vic.ai stood in it.
Sage Intacct first, Vic.ai an alternative
9 of 14 modelsVic.ai was named in the answer but not as the choice, or not at all.Claude Haiku 4.5Sage Intacct alternatives: Ramp Accounting Agent, Rillet, Xero
GPT-5.4 miniSage Intacct alternatives: FloQast, HighRadius
Perplexity SonarRillet, Sage Intacct alternatives: Vic.ai, Zeni
Grok 4.1 FastSage Intacct alternatives: Oracle NetSuite, Xero
Qwen 3.7 FlashSage Intacct alternatives: BlackLine, Bluecopa, Oracle NetSuite, Vic.ai
Kimi K2Sage Intacct alternatives: BILL, Oracle NetSuite, Ramp Accounting Agent, Rillet
GLM 4.7 FlashXSage Intacct, Xero alternatives: Oracle NetSuite, Ramp Accounting Agent, Rillet
MiniMax M2.5Sage Intacct alternatives: Booke AI, Oracle NetSuite, Vic.ai
GPT-6 LunaSage Intacct alternatives: Intuit Enterprise Suite, Oracle NetSuite
Vic.ai first, Sage Intacct an alternative
2 of 14 modelsSage Intacct was named in the answer but not as the choice, or not at all.Mistral SmallVic.ai alternatives: Oracle NetSuite, Sage Intacct
Llama 4 MaverickVic.ai alternatives: BILL, Oracle NetSuite, Sage Intacct
Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.Gemini 3.5 FlashTruewind alternatives: Numeric, Ramp Accounting Agent, Stampli, Vic.ai
DeepSeek V4 FlashRillet alternatives: Digits, Pilot, Sage Intacct
Muse Glimmer 30BRillet alternatives: Sage Intacct
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
Sage Intacct is not named for this buyer.
Sage Intacct—not namedVic.ai0%#14 of 14Mid-marketThe figures above
Sage Intacct leads by thirteen points.
Sage Intacct22%#1 of 17Vic.ai9%#4 of 17Enterprise
Level: the same share of first choices.
Sage Intacct15%#2 of 12Vic.ai15%#1 of 12What the models said about Sage Intacct
No label in this category carried a quote.
What the models said about Vic.ai
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Both claim up to 95% autonomy for AP tasks and bookkeeping, but these figures should be treated as marketing claims rather than guarantees.” Mistral Small · negative prompt · soft negative
“Overhyped Autonomy Claims (e.g., Booke AI, Vic.ai claiming 95% hands-off)” Grok 4.1 Fast · negative prompt · soft negative
“1. Vic.ai - Autonomous AP at real enterprise invoice volume, with invoice coding cost drops from ~$12 to under $2 each.” Llama 4 Maverick · paraphrase prompt · first choice
“Vic.ai is often cited as the best AI-driven tool for bookkeeping, especially for AP automation” Mistral Small · direct prompt · first choice
“I'd recommend Vic.ai as a top choice for a mid-sized B2B company” MiniMax M2.5 · paraphrase prompt · first choice
Also compared
Sage Intacct vs RilletSage Intacct vs Zoho BooksSage Intacct vs WaveSage Intacct vs NumericSage Intacct vs XeroSage Intacct vs DigitsRillet vs Vic.aiZoho Books vs Vic.aiVic.ai vs WaveVic.ai vs NumericVic.ai vs XeroVic.ai vs DigitsAll twenty-eight head-to-head pages and the full AI accounting assistants standing
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.