Bonusly vs Achievers
Ten of fourteen models named Bonusly first on the direct prompt; zero named Achievers. Both were named by all fourteen models and Bonusly carries 64 labels and Achievers 27, so the shares are not directly comparable.
Named in six categories this edition.
Named in two categories 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 recognition and rewards page.
By framing
BonuslyFirst choices, of fourteen modelsAchievers
Across every category in the November 2026 Edition, Bonusly and Achievers were named in the same answer seventy-eight times, of the 222 answers naming Bonusly and the 115 naming Achievers. In those answers Achievers took the first choice twelve times and Bonusly eighteen.
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 | | | | |
Bonusly Achievers first choice named as an alternative argued againstblank: not namedEach cell is one answer, Bonusly on the left and Achievers on the right.The direct prompt
Bonusly first, Achievers not the choice
10 of 14 modelsAchievers was named in the answer but not as the choice, or not at all.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.Neither was named
1 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
Bonusly61%#1 of 13Achievers0%#12 of 13Bonusly58%#1 of 10Achievers2%#6 of 10Achievers21%#3 of 10Bonusly5%#4 of 10What the models said about Bonusly
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Bonusly — *Best for Peer-to-Peer Engagement & Culture Building* ... famous for its high adoption rates and lightweight, fun user experience” Gemini 3.5 Flash · direct prompt · first choice
“Bonusly | High-frequency peer recognition | Very easy to use, strong Slack/Teams integration, gamified feel. Great for rapid adoption.” Qwen 3.7 Flash · scale prompt · first choice
“Bonusly – Highly rated for its ease of use, integrations with HRIS and workplace tools, and flexible reward options” Mistral Small · direct prompt · first choice
What the models said about Achievers
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Enterprise-grade platforms (like older versions of Achievers or smaller boutique providers) that charge high setup fees or lock you into annual contracts” Qwen 3.7 Flash · negative prompt · soft negative
“but it may be overkill (and pricier) for pure mid-sized use” Grok 4.20 · paraphrase prompt · soft negative
“Not overly enterprise-heavy like Achievers or Workhuman” Grok 4.20 · direct prompt · soft negative
“Choose Workhuman/Achievers if you need deep analytics, global scalability, and alignment with formal performance goals.” Qwen 3.7 Flash · comparative prompt · first choice
“Best Overall: Achievers” Claude Haiku 4.5 · paraphrase prompt · first choice
“Large or multinational organizations | More structured, enterprise-oriented programs... Likely more platform and setup than a smaller team needs.” GPT-6 Luna · comparative prompt · alternative
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.