Enable vs e-bate
Ten of fourteen models named Enable first on the direct prompt; zero named e-bate. Enable was named by fourteen of the fourteen models and e-bate by twelve and Enable carries 62 labels and e-bate 23, so the shares are not directly comparable.
Named in one category 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 rebate management page.
By framing
EnableFirst choices, of fourteen modelse-bate
Across every category in the November 2026 Edition, Enable and e-bate were named in the same answer forty-nine times, of the 193 answers naming Enable and the 61 naming e-bate. In those answers e-bate took the first choice nine times and Enable twenty-three.
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 4.20 | | | | | | |
| 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 | | | | |
Enable e-bate first choice named as an alternative argued againstblank: not namedEach cell is one answer, Enable on the left and e-bate on the right.The direct prompt
Enable first, e-bate an alternative
10 of 14 modelse-bate was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
4 of 14 modelsThe answer put something else first and named one of the two as an alternative.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
What the models said about Enable
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoid starting with Enable, Vendavo, Vistex, or Model N” Perplexity Sonar · budget prompt · hard negative
“strong overall reputation, but recent G2 feedback flags data duplication, timezone/report access issues, difficult implementation, a steep learning curve, and clunky navigation” GPT-5.4 mini · negative prompt · soft negative
“Implementation Disconnects: Users report friction during onboarding where pre-sales expectations clash with data mapping realities” Gemini 3.5 Flash · negative prompt · soft negative
“Most enterprise buyers today evaluate Enable as the category leader (consistently ranked #1 in G2 grids and cited as a clear leader in AI recommendation indexes)” Kimi K2 · comparative prompt · first choice
“Enable: A top choice for mid-market distributors, manufacturers, and retailers seeking strong trading-partner collaboration and dedicated rebate management.” Gemini 3.5 Flash · scale prompt · first choice
“Enable is best for enterprise rebate operations, as an AI-powered pricing and rebate management platform built for complex commercial teams” Claude Haiku 4.5 · comparative prompt · first choice
What the models said about e-bate
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“I'd put e-bate on your shortlist. It focuses on rebate workflows and supports both buy-side and sell-side rebates” GPT-6 Luna · budget prompt · first choice
“e-bate if you need a dedicated rebate-first system and want to avoid per-user costs and want rapid time-to-value.” Muse Glimmer 30B · budget prompt · first choice
“e-Bate stands out as the best choice for budget-conscious companies.” Claude Haiku 4.5 · budget 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.