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Contact center quality assurance · October 2026 Edition

Scorebuddy vs CloudTalk

Five of fourteen models named Scorebuddy first on the direct prompt; zero named CloudTalk. Scorebuddy was named by twelve of the fourteen models and CloudTalk by nine and Scorebuddy carries 31 labels and CloudTalk 15, so the shares are not directly comparable.

Scorebuddy

accepted challenger

Named in one category this edition.

CloudTalk

accepted challenger

Named in thirteen categories this edition.

First-choice share18%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate3%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#5A position in a field of 13; printed, not drawn.
Labels3115A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Scorebuddy reading right to left. Rank and label count are printed, not drawn.EvaluAgent was named alongside these two in twelve of the fourteen direct answers. EvaluAgent vs Scorebuddy · EvaluAgent vs CloudTalk · Scorebuddy vs Zendesk QA

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
ScorebuddyFirst choices, of fourteen modelsCloudTalk
Direct50
Paraphrase30
Comparative00
Budget-constrained12
Scale-constrained00
Negative001 against Scorebuddy
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Scorebuddy and CloudTalk stood in it.
ModelDirectSCParaphraseSCComparativeSCBudget-constrainedSCScale-constrainedSCNegativeSC
Claude Haiku 4.5SC
GPT-5.4 miniSC
Gemini 3.5 FlashSC
Perplexity SonarSCSC
Grok 4.1 FastSCSCSCSC
Mistral SmallSCSC
DeepSeek V4 FlashSCSC
Llama 4 MaverickSC
Qwen 3.7 Flash
Kimi K2SCSCSC
GLM 4.7 FlashXSCSC
MiniMax M2.5SC
GPT-6 Luna
Muse Glimmer 30BSCSC
SC Scorebuddy CloudTalkSC first choiceSC named as an alternativeSC argued againstblank: not namedEach cell is one answer, Scorebuddy on the left and CloudTalk on the right.

The direct prompt

The plain question, one answer per model, grouped by where Scorebuddy and CloudTalk stood in it.

Scorebuddy first, CloudTalk not the choice

5 of 14 modelsCloudTalk was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Scorebuddy alternatives: Convin, EvaluAgent, Gong, Level AI
Perplexity SonarScorebuddy alternatives: AmplifAI, Balto, EvaluAgent, Level AI
Grok 4.1 FastEvaluAgent, Scorebuddy alternatives: Balto, Talkdesk QM, Zendesk QA
Mistral SmallEvaluAgent, Scorebuddy alternatives: Talkdesk
MiniMax M2.5Scorebuddy alternatives: EvaluAgent, Voxjar, Zendesk QA

Neither was the first choice, one was named

5 of 14 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniLevel AI alternatives: Calabrio ONE, Scorebuddy, Zendesk QA
DeepSeek V4 FlashEvaluAgent alternatives: AmplifAI, Observe.AI, Scorebuddy, Talkdesk, Zendesk QA
Llama 4 MaverickEvaluAgent alternatives: GetApp, Scorebuddy
GLM 4.7 FlashXAmplifAI, Balto alternatives: EvaluAgent, Playvox QM, Scorebuddy
Muse Glimmer 30BObserve.AI, Zendesk QA alternatives: EvaluAgent, Playvox, Scorebuddy

Neither was named

4 of 14 modelsThe answer made no first choice from these two in this category.
Gemini 3.5 FlashZendesk QA alternatives: EvaluAgent, Level AI, MaestroQA
Qwen 3.7 FlashCallMiner Eureka alternatives: Five9 Agent Connect, MaestroQA, NICE CXone Quality Management, Playvox
Kimi K2EvaluAgent alternatives: Playvox, ScorebuddyCX, Zendesk QA
GPT-6 LunaEvaluAgent alternatives: CallMiner Eureka, NICE CXone Quality Management

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
CloudTalk leads by seven points.
CloudTalk15%#2 of 12
Scorebuddy7%#5 of 12
The full small business standing →
Mid-marketThe figures above
The order flips: Scorebuddy leads at mid-market.
Scorebuddy18%#2 of 13
CloudTalk4%#5 of 13
The full mid-market standing →
Enterprise
CloudTalk is not named for this buyer.
Scorebuddy2%#10 of 14
CloudTalk—not named
The full enterprise standing →

What 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.

“Suggest the vendor isn't investing in the product (mentioned about some tools like Scorebuddy)” Kimi K2 · negative prompt · soft negative
“Scorebuddy (now ScorebuddyCX) stands out as one of the best contact center quality assurance (QA) tools” Grok 4.1 Fast · budget prompt · first choice
“EvaluAgent and Scorebuddy are often highlighted as the most cost-effective and scalable options” Mistral Small · direct prompt · first choice
“I'd recommend Scorebuddy as a strong starting point for support QA and conversation review.” Grok 4.1 Fast · paraphrase prompt · first choice

What the models said about CloudTalk

No label in this category carried a quote.

Also compared

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.