Four of fourteen models named Sage Intacct Revenue Recognition first on the direct prompt; zero named NetSuite Advanced Revenue Management. Sage Intacct Revenue Recognition was named by thirteen of the fourteen models and NetSuite Advanced Revenue Management by thirteen and Sage Intacct Revenue Recognition carries 38 labels and NetSuite Advanced Revenue Management 31, so the shares are not directly comparable.
Named in one category this edition.
Named in two categories this edition.
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 revenue recognition page.
Across every category in the October 2026 Edition, Sage Intacct Revenue Recognition and NetSuite Advanced Revenue Management were named in the same answer forty-two times, of the 80 answers naming Sage Intacct Revenue Recognition and the 88 naming NetSuite Advanced Revenue Management. In those answers NetSuite Advanced Revenue Management took the first choice three times and Sage Intacct Revenue Recognition four.
| 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 |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“avoiding expensive enterprise resource planning (ERP) platforms (like NetSuite or Sage Intacct, which can cost $10,000 to over $100,000 annually)” Gemini 3.5 Flash · budget prompt · hard negative
“Avoid enterprise-grade solutions (Zuora Revenue, NetSuite ARM, Sage Intacct)” DeepSeek V4 Flash · budget prompt · hard negative
“Lacks reconciliation depth for contract modifications and multi-element arrangements ... Verdict: Consider carefully if you have complex subscription models” Kimi K2 · negative prompt · soft negative
“Highly popular in the mid-market B2B SaaS and professional services space. It handles ASC 606 compliance natively inside the ledger” Gemini 3.5 Flash · direct prompt · first choice
“Top Recommendation: Sage Intacct Revenue Recognition — Best overall for mid-market B2B.” DeepSeek V4 Flash · direct prompt · first choice
“Sage Intacct or BillingPlatform — both offer strong mid-market functionality” Kimi K2 · paraphrase prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“avoiding this module and opting for an external, agile middleware tool (like RightRev or Leapfin) is often safer” Gemini 3.5 Flash · negative prompt · hard negative
“Avoid enterprise-grade solutions (Zuora Revenue, NetSuite ARM, Sage Intacct)” DeepSeek V4 Flash · budget prompt · hard negative
“ERP-native modules (like NetSuite ARM) are exceptionally powerful but often require highly specialized consultants, costing upwards of $50k–$250k” Gemini 3.5 Flash · scale prompt · soft negative
“Best for ERP-Native Compliance: NetSuite Advanced Revenue Management (ARM)” Gemini 3.5 Flash · paraphrase prompt · first choice
“You already run NetSuite and your revenue rules are relatively straightforward | NetSuite Advanced Revenue Management” GPT-6 Luna · direct prompt · alternative
“It is the default choice for companies scaling on NetSuite... but requires highly disciplined upstream data entry.” Gemini 3.5 Flash · comparative prompt · alternative
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.