Zero of fourteen models named FeedbackPulse first on the direct prompt; zero named 15Five. FeedbackPulse was named by eleven of the fourteen models and 15Five by fourteen and FeedbackPulse carries 18 labels and 15Five 40, so the shares are not directly comparable.
Named in three categories this edition.
Named in nine 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 360 feedback 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. Three of four in this category shown.
“Start with FeedbackPulse (free tier) if you have fewer than 10 employees.” GLM 4.7 FlashX · budget prompt · first choice
“Teamflect (if you use Microsoft Teams) or FeedbackPulse” Kimi K2 · budget prompt · first choice
“FeedbackPulse is the strongest overall choice” Perplexity Sonar · 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.
“Avoid enterprise tools like Lattice, 15Five, or Culture Amp” Grok 4.20 · budget 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.