Two of fourteen models named ManageEngine AssetExplorer first on the direct prompt; one named ServiceNow IT Asset Management. Both were named by all fourteen models and ManageEngine AssetExplorer carries 43 labels and ServiceNow IT Asset Management 44, so the shares are not directly comparable.
Named in three categories 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 IT asset management page.
Across every category in the November 2026 Edition, ManageEngine AssetExplorer and ServiceNow IT Asset Management were named in the same answer sixty-five times, of the 108 answers naming ManageEngine AssetExplorer and the 150 naming ServiceNow IT Asset Management. In those answers ServiceNow IT Asset Management took the first choice twenty-nine times and ManageEngine AssetExplorer three.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | ||||||
| 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 seven in this category shown.
“ManageEngine may lack scalability for large organizations, and advanced features like asset discovery require additional modules” Claude Haiku 4.5 · negative prompt · soft negative
“AssetExplorer cloud pricing starts at $955/year for 250 assets, so it may cost more than Snipe-IT.” GPT-6 Luna · budget prompt · soft negative
“I'd be most cautious about AssetExplorer and Ivanti on security operations” GPT-6 Luna · negative prompt · soft negative
“My overall recommendation for most mid-market B2B companies: If you want a well-rounded, cost-effective solution with strong features — ManageEngine AssetExplorer.” DeepSeek V4 Flash · direct prompt · first choice
“I'd recommend ManageEngine AssetExplorer as the top choice for your mid-sized B2B company” GLM 4.7 FlashX · paraphrase prompt · first choice
“a balanced choice is ManageEngine AssetExplorer (cost-effective, solid core features)” GLM 4.7 FlashX · direct prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Multiple AI-driven reviews recommend avoiding ServiceNow unless you are a large enterprise with dedicated administrators and a substantial budget.” Mistral Small · negative prompt · hard negative
“Avoid purchasing top-tier enterprise platforms (e.g., ServiceNow) unless you have dedicated full-time administrative staff” Gemini 3.5 Flash · direct prompt · hard negative
“Avoid enterprise platforms such as ServiceNow unless the company needs their extensive ITSM and governance capabilities” Perplexity Sonar · budget prompt · hard negative
“ServiceNow received the highest rating of 8.6 among the leaders, is the most popular solution in terms of searches by peers, and holds the largest mind share of 11.8%.” Muse Glimmer 30B · comparative prompt · first choice
“It is widely considered the "Gold Standard" because it unifies Hardware Asset Management (HAM), Software Asset Management (SAM), and Cloud Insights” GLM 4.7 FlashX · comparative prompt · first choice
“Often considered the "Gold Standard" of ITAM... ServiceNow ITAM leads for enterprise integration” MiniMax M2.5 · comparative prompt · first choice
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.