Bonusly vs Awardco
Three of twelve models named Bonusly first on the direct prompt; one named Awardco. Both were named by all twelve models and Bonusly carries 48 labels and Awardco 39, so the shares are not directly comparable.
Named in four categories 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 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 recognition and rewards page.
By framing
BonuslyFirst choices, of twelve modelsAwardco
Across every category in the September 2026 Edition, Bonusly and Awardco were named in the same answer eighty times, of the 166 answers naming Bonusly and the 114 naming Awardco. In those answers Awardco took the first choice ten times and Bonusly nineteen.
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 | | | |
Bonusly Awardco first choice named as an alternative argued againstblank: not namedEach cell is one answer, Bonusly on the left and Awardco on the right.The direct prompt
Bonusly first, Awardco an alternative
3 of 12 modelsAwardco was named in the answer but not as the choice, or not at all.Awardco first, Bonusly an alternative
1 of 12 modelsBonusly was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
5 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 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
Bonusly60%#1 of 8Awardco7%#5 of 8Awardco17%#3 of 10Bonusly2%#5 of 10What the models said about Bonusly
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Desktop-focused, limited custom rewards, basic analytics, and required monthly point caps that can add unexpected costs.” Mistral Small · negative prompt · soft negative
“start with Matter (free or $1/user/month) or Bonusly (free plan available). Both are highly rated” Mistral Small · budget prompt · first choice
“For most small companies with limited budgets, Bonusly offers the best balance” DeepSeek V4 Flash · budget prompt · first choice
“Bonusly — *Best Overall for High Adoption and B2B Tech Cultures*” Gemini 3.5 Flash · direct prompt · first choice
What the models said about Awardco
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Avoid: Enterprise tools like Awardco ($2,500+/year min.)” Grok 4.1 Fast · budget prompt · hard negative
“treat this as "be cautious on catalog quality," not "avoid entirely."” DeepSeek V4 Flash · negative prompt · soft negative
“(Nectar, Awardco, Motivosity) require annual minimum spends” DeepSeek V4 Flash · budget prompt · soft negative
“Best For: Mid-market and large enterprise organizations looking for massive reward choices and budget controls... Zero-markup rewards.” Gemini 3.5 Flash · comparative prompt · first choice
“Highest-rated overall for mid-market scaling. Amazon integration for massive rewards catalog” Grok 4.1 Fast · direct prompt · first choice
“I'd recommend Awardco as the top employee rewards program software” Grok 4.1 Fast · paraphrase 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.