Zendesk QA vs Level AI
Two of fourteen models named Zendesk QA first on the direct prompt; one named Level AI. Zendesk QA was named by thirteen of the fourteen models and Level AI by nine and Zendesk QA carries 28 labels and Level AI 12, so the shares are not directly comparable.
By Zendesk, San Francisco, United States, founded 2007. Named in one category this edition.
Named in four 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 contact center quality assurance page.
By framing
Zendesk QAFirst choices, of fourteen modelsLevel AI
Across every category in the October 2026 Edition, Zendesk QA and Level AI were named in the same answer twelve times, of the 84 answers naming Zendesk QA and the 35 naming Level AI. In those answers Level AI took the first choice three times and Zendesk QA three.
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 | | |
Zendesk QA Level AI first choice named as an alternative argued againstblank: not namedEach cell is one answer, Zendesk QA on the left and Level AI on the right.The direct prompt
Zendesk QA first, Level AI an alternative
2 of 14 modelsLevel AI was named in the answer but not as the choice, or not at all.Level AI first, Zendesk QA an alternative
1 of 14 modelsZendesk QA 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
5 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 QA9%#4 of 12Level AI0%#– of 12Zendesk QA14%#3 of 13Level AI2%#8 of 13Zendesk QA4%#7 of 14Level AI4%#8 of 14What 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.
“Tools that rely heavily on AI scoring (e.g., Observe.AI, Kaizo, Zendesk QA/Formerly Klaus)” DeepSeek V4 Flash · negative prompt · soft negative
“If you are Zendesk-native: Zendesk QA, formerly Klaus ... Zendesk QA gives the deepest native integration and risk-based review without manual sampling.” Muse Glimmer 30B · direct prompt · first choice
“A highly automated, AI-driven QA tool built specifically for support teams, now natively unified inside the Zendesk ecosystem.” Gemini 3.5 Flash · comparative prompt · first choice
“I'd recommend Zendesk QA (formerly Klaus) if your priority is support quality assurance and conversation review” Perplexity Sonar · paraphrase prompt · first choice
What the models said about Level AI
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“my default recommendation is Level AI if you want the most balanced mix of AI-powered QA, omnichannel coverage, and mid-market fit” GPT-5.4 mini · direct prompt · first choice
“often ranked #1 for comprehensive QA” DeepSeek V4 Flash · comparative prompt · first choice
“Level AI is recommended for mid-market contact centers consolidating conversation intelligence and QA on one platform” Claude Haiku 4.5 · direct 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.