Index › GTM › Revenue operations › Territory planning › Everstage vs Lative
Territory and quota planning · November 2026 Edition

Everstage vs Lative

Zero of fourteen models named Everstage first on the direct prompt; one named Lative. Everstage was named by ten of the fourteen models and Lative by nine and Everstage carries 10 labels and Lative 13, so the shares are not directly comparable.

Everstage

accepted challenger

Named in three categories this edition.

Lative

accepted challenger

Named in one category this edition.

First-choice share8%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%23%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#7A position in a field of 13; printed, not drawn.
Labels1013A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Everstage reading right to left. Rank and label count are printed, not drawn.CaptivateIQ Planning was named alongside these two in fourteen of the fourteen direct answers. CaptivateIQ Planning vs Everstage · CaptivateIQ Planning vs Lative · Everstage vs Fullcast Plan

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
EverstageFirst choices, of fourteen modelsLative
Direct01
Paraphrase40
Comparative00
Budget-constrained003 against Lative
Scale-constrained00
Negative00
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Everstage and Lative stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Everstage Lative first choice named as an alternative argued againstblank: not namedEach cell is one answer, Everstage on the left and Lative on the right.

The direct prompt

The plain question, one answer per model, grouped by where Everstage and Lative stood in it.

Lative first, Everstage not the choice

1 of 14 modelsEverstage was named in the answer but not as the choice, or not at all.
Mistral SmallCaptivateIQ Planning, Lative alternatives: Fullcast Plan, Xactly, ZoomInfo

Neither was named

13 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5CaptivateIQ Planning alternatives: Maptive, Pipedrive, Salesforce Maps, Salesforce Sales Planning, eSpatial
GPT-5.4 miniAnaplan Territory & Quota Planning alternatives: CaptivateIQ Planning, Vena
Gemini 3.5 FlashFullcast Plan alternatives: CaptivateIQ Planning, Pigment, Salesforce Maps, Salesforce Sales Planning, Xactly, Xactly AlignStar
Perplexity SonarCaptivateIQ Planning alternatives: Fullcast Plan, Pigment, QuotaPath
Grok 4.20CaptivateIQ Planning alternatives: Fullcast Plan, QuotaPath
DeepSeek V4 FlashCaptivateIQ Planning alternatives: Fullcast Plan, Pigment
Llama 4 MaverickCaptivateIQ Planning
Qwen 3.7 FlashCaptivateIQ Planning alternatives: Fullcast Plan, QuotaPath, Visdum, Xactly
Kimi K2CaptivateIQ Planning alternatives: QuotaPath, Visdum
GLM 4.7 FlashXCaptivateIQ Planning, Fullcast Plan alternatives: QuotaPath, Salesforce Sales Planning
MiniMax M2.5CaptivateIQ Planning alternatives: QuotaPath, Vena, Xactly
GPT-6 LunaFullcast Plan alternatives: CaptivateIQ Planning, Salesforce Sales Planning
Muse Glimmer 30BCaptivateIQ Planning alternatives: QuotaPath

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Lative leads by two points.
Lative3%#8 of 16
Everstage2%#– of 16
The full small business standing →
Mid-marketThe figures above
The order flips: Everstage leads at mid-market.
Everstage8%#2 of 13
Lative2%#7 of 13
The full mid-market standing →
Enterprise
The order flips: Lative leads at enterprise.
Lative2%#– of 9
Everstage0%#– of 9
The full enterprise standing →

What the models said about Everstage

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.

“CaptivateIQ, Everstage, and Performio offer strong capability with faster deployment cycles and more competitive pricing” Claude Haiku 4.5 · paraphrase prompt · first choice

What the models said about Lative

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.

“Other options include Pigment, Lative, and Anaplan, but they may be more expensive or have more complex pricing structures.” Llama 4 Maverick · budget prompt · soft negative
“From $5,000/year (up to 20 reps) | Tying territory design directly to quota and rep capacity” Kimi K2 · budget prompt · soft negative
“Lative (from ~$5k/year but scalable)” Grok 4.20 · budget prompt · soft negative
“CaptivateIQ Planning and Lative are the top choices” Mistral Small · direct prompt · first choice
“Coverage-based planning around your reps, fast, affordable ($5K/yr) | If you need to start small and simple” DeepSeek V4 Flash · scale prompt · alternative
“Lative (~$5,000/yr) — it uniquely connects territory design to actual rep capacity and quota fairness” Qwen 3.7 Flash · budget prompt · alternative
Also compared

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.