e-bate vs Voucherify
One of fourteen models named e-bate first on the direct prompt; zero named Voucherify. e-bate was named by twelve of the fourteen models and Voucherify by thirteen and e-bate carries 23 labels and Voucherify 18, 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
e-bateFirst choices, of fourteen modelsVoucherify
Across every category in the October 2026 Edition, e-bate and Voucherify were named in the same answer thirty-six times, of the 65 answers naming e-bate and the 55 naming Voucherify. In those answers Voucherify took the first choice three times and e-bate thirteen.
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 | |
e-bate Voucherify first choice named as an alternative argued againstblank: not namedEach cell is one answer, e-bate on the left and Voucherify on the right.The direct prompt
e-bate first, Voucherify not the choice
1 of 14 modelsVoucherify was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
1 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
12 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
e-bate26%#1 of 11Voucherify17%#3 of 11What 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.
“e‑bate – Uses a custom quote model (not per‑user), which can be more predictable for SMBs. It focuses on rebate automation and audit control” GLM 4.7 FlashX · budget prompt · first choice
“e-bate — best fit for a limited budget focused specifically on rebates.” GPT-5.4 mini · budget prompt · first choice
“e-bate is the most frequently cited affordable rebate-first platform” Muse Glimmer 30B · budget prompt · first choice
What the models said about Voucherify
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Voucherify – Publishes transparent, tiered pricing with a free tier for low volume. The Business plan at $650/month gives solid coverage without enterprise-level cost.” GLM 4.7 FlashX · budget prompt · first choice
“This platform is highly recommended for smaller teams that want to launch rebate programs quickly through APIs” Mistral Small · budget prompt · first choice
“Voucherify is the clearest budget-friendly recommendation for a smaller team” Perplexity Sonar · 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.