Maptive vs Fullcast
Zero of fourteen models named Maptive first on the direct prompt; one named Fullcast. Maptive was named by eight of the fourteen models and Fullcast by twelve and Maptive carries 11 labels and Fullcast 24, 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 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 territory and quota planning page.
By framing
MaptiveFirst choices, of fourteen modelsFullcast
Across every category in the October 2026 Edition, Maptive and Fullcast were named in the same answer twenty-two times, of the 41 answers naming Maptive and the 76 naming Fullcast. In those answers Fullcast took the first choice three times and Maptive six.
Every model, every framing
| 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 | |
Maptive Fullcast first choice named as an alternative argued againstblank: not namedEach cell is one answer, Maptive on the left and Fullcast on the right.The direct prompt
Fullcast first, Maptive not the choice
1 of 14 modelsMaptive was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
8 of 14 modelsThe answer made no first choice from these two in this category.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.
By buyer segment
Maptive6%#3 of 11Fullcast4%#4 of 11Maptive2%#– of 9Fullcast2%#7 of 9What the models said about Maptive
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“When to Avoid: If your sales structure is based on industry verticals, company size, or intent data rather than geography.” Gemini 3.5 Flash · negative prompt · soft negative
“good for basic mapping but limited in complex territory balancing and quota workflows” DeepSeek V4 Flash · negative prompt · soft negative
“Maptive stands out as one of the best affordable sales territory and quota planning tools” Grok 4.1 Fast · budget prompt · first choice
“Top Recommendation: Maptive (Best overall for limited budgets)” DeepSeek V4 Flash · budget prompt · first choice
“Top Recommendation: Maptive” Qwen 3.7 Flash · budget prompt · first choice
What the models said about Fullcast
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“$20K–$60K/year. Not budget-friendly.” DeepSeek V4 Flash · budget prompt · hard negative
“Dedicated tools like Fullcast or Anaplan are powerful but start at custom quotes often >$10k/year—unsuitable for limited budgets.” Grok 4.1 Fast · budget prompt · soft negative
“the research suggests a dedicated territory engine (like Fullcast, BoogieBoard, or similar) paired with clean data may be a better fit” DeepSeek V4 Flash · negative prompt · first choice
“Fullcast stands out for AI‑driven territory design and native Salesforce plan‑to‑pay integration.” GLM 4.7 FlashX · comparative prompt · first choice
“Prioritize tools with quota-territory linkage (e.g., Fullcast for mid-market, Anaplan for enterprise)” Grok 4.1 Fast · negative 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.