Zero of fourteen models named Faddom first on the direct prompt; one named Zscaler Private Access. Faddom was named by nine of the fourteen models and Zscaler Private Access by eight and Faddom carries 11 labels and Zscaler Private Access 14, so the shares are not directly comparable.
Named in two categories this edition.
Named in three 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 microsegmentation page.
| 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. Four of five in this category shown.
“Mapping only, not full enforcement” DeepSeek V4 Flash · direct prompt · soft negative
“The best microsegmentation solution for a company with a limited budget is Faddom, which is a budget-friendly dependency mapping tool.” Llama 4 Maverick · budget prompt · first choice
“For a company with limited budget, I'd recommend starting with Faddom” MiniMax M2.5 · budget prompt · first choice
“start with Enclave (free) or Faddom (low-cost)” Mistral Small · budget prompt · first choice
No label in this category carried a quote.
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.