eSpatial vs Varicent
Zero of twelve models named eSpatial first on the direct prompt; zero named Varicent. eSpatial was named by eleven of the twelve models and Varicent by twelve and eSpatial carries 28 labels and Varicent 36, so the shares are not directly comparable.
Named in one category this edition.
Named in two categories 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.
By framing
eSpatialFirst choices, of twelve modelsVaricent
Across every category in the September 2026 Edition, eSpatial and Varicent were named in the same answer forty times, of the 65 answers naming eSpatial and the 173 naming Varicent. In those answers Varicent took the first choice five times and eSpatial two.
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 | | |
eSpatial Varicent first choice named as an alternative argued againstblank: not namedEach cell is one answer, eSpatial on the left and Varicent on the right.The direct prompt
Neither was the first choice, one was named
4 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
8 of 12 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
eSpatial2%#6 of 13Varicent0%#12 of 13eSpatial2%#5 of 11Varicent2%#7 of 11Varicent35%#1 of 10eSpatial0%#9 of 10What the models said about eSpatial
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Mapping-first tools ... but no quota or capacity planning. Good only if territories are purely geographic.” DeepSeek V4 Flash · direct prompt · soft negative
“Great tools but start at $1,495–$2,400/year, only economical if you need algorithmic territory balancing.” DeepSeek V4 Flash · budget prompt · soft negative
“is likely a step up in cost and complexity” Perplexity Sonar · budget prompt · soft negative
“🏆 Top Recommendation: eSpatial” Qwen 3.7 Flash · budget prompt · first choice
“eSpatial is best for mid-market teams that want algorithmic territory balancing and clear visual mapping” Claude Haiku 4.5 · budget prompt · alternative
“Look for tools (like eSpatial or Varicent) that use algorithms to automatically assign accounts” Qwen 3.7 Flash · scale prompt · alternative
What the models said about Varicent
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“What to avoid for mid-market ... Varicent / Xactly / Oracle SPM — Full sales-performance suites costing $50K–$100K+/year” DeepSeek V4 Flash · direct prompt · hard negative
“Skip these (too expensive for a limited budget)” DeepSeek V4 Flash · budget prompt · hard negative
“Varicent Territory Sales Planning supports complex territory modeling, robust scenario modeling, and AI-assisted planning” Claude Haiku 4.5 · comparative prompt · first choice
“Varicent - Strongest for complex orgs needing linked territory/quota planning with AI insights” Mistral Small · scale prompt · first choice
“Best for: Enterprises linking territory/quota to incentive compensation (ICM), with AI whitespace analysis.” Grok 4.1 Fast · comparative 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.