Zero of twelve models named Everstage first on the direct prompt; zero named Maptive. Everstage was named by seven of the twelve models and Maptive by eleven and Everstage carries 12 labels and Maptive 21, so the shares are not directly comparable.
Named in two categories this edition.
Named in one category this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 territory and quota planning 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 |
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. Four of five in this category shown.
“Mapping-first tools with algorithmic territory balancing at lower cost, but no quota or capacity planning” DeepSeek V4 Flash · direct prompt · soft negative
“Maptive is best for teams that want every mapping feature included at a flat per-user price” Claude Haiku 4.5 · budget prompt · first choice
“"We just need to draw clean maps and visualize coverage" → eSpatial, Maptive, or Salesforce Maps” Qwen 3.7 Flash · comparative prompt · alternative
“eSpatial, Maptive | Excellent for algorithmic balancing and deep GIS visualization.” Qwen 3.7 Flash · scale prompt · alternative
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.