Two of fourteen models named Microsoft Defender for Cloud first on the direct prompt; one named Aqua Security. Microsoft Defender for Cloud was named by fourteen of the fourteen models and Aqua Security by eight and Microsoft Defender for Cloud carries 53 labels and Aqua Security 12, so the shares are not directly comparable.
Named in fourteen categories this edition.
Named in five 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 cloud-native application protection 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. One of one in this category shown.
“The Trap: Relying exclusively on AWS Security Hub or Microsoft Defender for Cloud... Vendor Lock-in.” Qwen 3.7 Flash · negative prompt · hard negative
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“Aqua Security - a CNAPP that secures containerized and cloud-native applications from development through runtime.” Llama 4 Maverick · paraphrase 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.