Scorebuddy vs Zendesk QA
Two of fourteen models named Scorebuddy first on the direct prompt; two named Zendesk QA. Scorebuddy was named by eleven of the fourteen models and Zendesk QA by fourteen and Scorebuddy carries 20 labels and Zendesk QA 38, so the shares are not directly comparable.
Scorebuddy
accepted challengerNamed in one category this edition.
By Zendesk, San Francisco, United States, founded 2007. 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 contact center quality assurance page.
By framing
Across every category in the November 2026 Edition, Scorebuddy and Zendesk QA were named in the same answer thirty-eight times, of the 61 answers naming Scorebuddy and the 108 naming Zendesk QA. In those answers Zendesk QA took the first choice two times and Scorebuddy twelve.
Every model, every framing
| Model | DirectSC | ParaphraseSC | ComparativeSC | Budget-constrainedSC | Scale-constrainedSC | NegativeSC |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | SC | ||||
| GPT-5.4 mini | | SC | ||||
| Gemini 3.5 Flash | SC | | SC | SC | SC | |
| Perplexity Sonar | SC | | SC | |||
| Grok 4.20 | | | SC | |||
| Mistral Small | | | ||||
| DeepSeek V4 Flash | | | | |||
| Llama 4 Maverick | SC | |||||
| Qwen 3.7 Flash | | | | SC | ||
| Kimi K2 | | | | | ||
| GLM 4.7 FlashX | | | SC | |||
| MiniMax M2.5 | SC | | ||||
| GPT-6 Luna | | | | |||
| Muse Glimmer 30B | SC | | SC |
Zendesk QASC first choiceSC named as an alternativeSC argued againstblank: not namedEach cell is one answer, Scorebuddy on the left and Zendesk QA on the right.The direct prompt
Scorebuddy first, Zendesk QA an alternative
2 of 14 modelsZendesk QA was named in the answer but not as the choice, or not at all.Zendesk QA first, Scorebuddy not the choice
2 of 14 modelsScorebuddy was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
6 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
4 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
Zendesk QA11%#3 of 13Zendesk QA8%#6 of 17Zendesk QA2%#11 of 15What the models said about Scorebuddy
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“capable choices for human-led QA, but pricing is generally less transparent and may be less suitable for a very constrained budget” Perplexity Sonar · budget prompt · soft negative
“For very tight budgets, Scorebuddy and EvaluAgent offer transparent, predictable per-seat pricing.” Claude Haiku 4.5 · budget prompt · first choice
“For most mid-market B2B companies, Scorebuddy is the best dedicated contact-center QA choice.” Perplexity Sonar · direct prompt · first choice
“The best contact center quality assurance tool for a company with a limited budget is Scorebuddy.” Llama 4 Maverick · budget prompt · first choice
What the models said about Zendesk QA
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Great if you're already on Zendesk ... However, the per-agent pricing can add up.” DeepSeek V4 Flash · budget prompt · soft negative
“Zendesk QA if you're on Zendesk (best integration, best UX, reasonable price)” DeepSeek V4 Flash · direct prompt · first choice
“My default pick would be Zendesk QA—if your support team already runs on Zendesk.” GPT-6 Luna · paraphrase prompt · first choice
“My Top Recommendation: Zendesk QA (formerly Klaus)” Kimi K2 · 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.