One of fourteen models named Microsoft Defender for Cloud first on the direct prompt; one named CrowdStrike Falcon. Microsoft Defender for Cloud was named by nine of the fourteen models and CrowdStrike Falcon by ten and Microsoft Defender for Cloud carries 10 labels and CrowdStrike Falcon 14, so the shares are not directly comparable.
Named in fifteen categories this edition.
Named in ten 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 AI security platforms 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 4.20 | ||||||
| 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. One of one in this category shown.
“Practical starter stack for limited budget: Microsoft 365 Business Premium / Defender for Business for endpoint, identity and email protection.” Muse Glimmer 30B · budget prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“CrowdStrike Falcon (with Charlotte AI or Falcon AIDR) is often the top choice for mid-market B2B companies.” Grok 4.20 · direct 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.