Ten of fourteen models named Lattice first on the direct prompt; zero named 15Five. Lattice was named by fourteen of the fourteen models and 15Five by thirteen and Lattice carries 48 labels and 15Five 35, so the shares are not directly comparable.
Named in nineteen 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 360 feedback page.
Across every category in the October 2026 Edition, Lattice and 15Five were named in the same answer 259 times, of the 712 answers naming Lattice and the 327 naming 15Five. In those answers 15Five took the first choice twenty times and Lattice fifty-seven.
| 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.
No label in this category carried a quote.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“Reported limitations: Managers can't do group check-ins... Limited engagement survey customization” Kimi K2 · negative prompt · soft negative
“$11 per user per month, billed annually, which can be expensive if 360s are all you need” GPT-6 Luna · budget prompt · soft 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.