Zero of fourteen models named Grafana Cloud first on the direct prompt; one named Dynatrace. Grafana Cloud was named by thirteen of the fourteen models and Dynatrace by fourteen and Grafana Cloud carries 36 labels and Dynatrace 52, so the shares are not directly comparable.
Named in five categories this edition.
Named in fifteen 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 application performance monitoring 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. Two of two in this category shown.
“self-hosted solutions like Grafana (requiring significant maintenance effort)” Qwen 3.7 Flash · paraphrase prompt · soft negative
“Start with Grafana Cloud's free tier or New Relic's free tier to get started at $0 cost.” DeepSeek V4 Flash · 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.
“Often recommended to be avoided unless you are a large enterprise with a dedicated SRE team.” Mistral Small · negative prompt · hard negative
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.