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Rebate management · November 2026 Edition

e-bate vs 360insights

Zero of fourteen models named e-bate first on the direct prompt; zero named 360insights. e-bate was named by twelve of the fourteen models and 360insights by eight and e-bate carries 23 labels and 360insights 15, so the shares are not directly comparable.

e-bate

accepted challenger

Named in one category this edition.

360insights

accepted challenger

Named in one category this edition.

First-choice share9%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%7%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#6A position in a field of 15; printed, not drawn.
Labels2315A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, e-bate reading right to left. Rank and label count are printed, not drawn.Enable was named alongside these two in fourteen of the fourteen direct answers. Enable vs e-bate · Enable vs 360insights · incentX vs e-bate

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
e-bateFirst choices, of fourteen models360insights
Direct00
Paraphrase01
Comparative00
Budget-constrained50
Scale-constrained00
Negative001 against 360insights
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 e-bate and 360insights 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
e-bate 360insights first choice named as an alternative argued againstblank: not namedEach cell is one answer, e-bate on the left and 360insights on the right.

The direct prompt

The plain question, one answer per model, grouped by where e-bate and 360insights stood in it.

Neither was the first choice, one was named

3 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashEnable alternatives: 360insights, Flintfox, Level 6, Vendavo, e-bate, incentX
Perplexity SonarEnable alternatives: BlackCurve, Fielo Rebates, Pricefx, SAP Condition Contract Settlement, Vistex, e-bate
DeepSeek V4 FlashEnable alternatives: Flintfox, e-bate, incentX

Neither was named

11 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5SolveXia alternatives: Enable, Phocas Software, Pricefx
GPT-5.4 miniEnable alternatives: IMA360, Vendavo, Vistex
Grok 4.20Enable alternatives: BlackCurve Rebate Management, Fielo Rebates, SolveXia, incentX
Mistral SmallEnable, incentX alternatives: Pricefx, Vistaar SmartRebates
Llama 4 MaverickFielo alternatives: BlackCurve Rebate Management, Enable, SolveXia, incentX
Qwen 3.7 FlashBlackCurve Rebate Management, Fielo alternatives: Enable, Zilliant, incentX
Kimi K2Enable alternatives: incentX
GLM 4.7 FlashXEnable alternatives: BlackCurve, Fielo, Zilliant, incentX
MiniMax M2.5Enable alternatives: E2open, Pricefx, SAP Condition Contract Settlement, Zilliant
GPT-6 LunaEnable alternatives: IMA360, Pricefx, Vendavo
Muse Glimmer 30BWiser Promotions alternatives: Enable, Pricefx, Vendavo, Vistaar SmartRebates, Zilliant, incentX

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
e-bate leads by seventeen points.
e-bate17%#3 of 11
360insights0%#– of 11
The full small business standing →
Mid-marketThe figures above
e-bate leads by seven points.
e-bate9%#3 of 15
360insights2%#6 of 15
The full mid-market standing →
Enterprise
Level: the same share of first choices.
e-bate0%#– of 9
360insights0%#– of 9
The full enterprise standing →

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

What the models said about 360insights

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

“Be cautious if you are a distributor collecting supplier rebates rather than a manufacturer or brand paying channel incentives.” Perplexity Sonar · negative prompt · soft negative
“I'd recommend evaluating Enable or 360insights first if you want a focused, easy-to-implement solution” Claude Haiku 4.5 · paraphrase prompt · first choice
“Choose 360insights when partner incentives and indirect-channel execution matter more than traditional rebate accounting.” Perplexity Sonar · comparative prompt · alternative
“Best if your focus is heavily on channel partner ecosystems... may feel more channel-centric than pure rebate tracking.” Grok 4.20 · paraphrase 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.