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Contact center quality assurance · October 2026 Edition

Playvox QM vs Playvox

Zero of fourteen models named Playvox QM first on the direct prompt; zero named Playvox. Playvox QM was named by seven of the fourteen models and Playvox by eight and Playvox QM carries 10 labels and Playvox 16, so the shares are not directly comparable.

Playvox QM

accepted challenger

Named in one category this edition.

Playvox

accepted challenger

Named in one category this edition.

First-choice share6%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#7A position in a field of 13; printed, not drawn.
Labels1016A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Playvox QM 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 Playvox QM · EvaluAgent vs Playvox · Scorebuddy vs Playvox QM

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.
Playvox QMFirst choices, of fourteen modelsPlayvox
Direct00
Paraphrase11
Comparative00
Budget-constrained20
Scale-constrained00
Negative00
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 Playvox QM and Playvox stood in it.
ModelDirectPQPLParaphrasePQPLComparativePQPLBudget-constrainedPQPLScale-constrainedPQPLNegativePQPL
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 FastPLPL
Mistral SmallPQ
DeepSeek V4 FlashPLPLPQPL
Llama 4 MaverickPQ
Qwen 3.7 FlashPLPQ
Kimi K2PLPLPLPQ
GLM 4.7 FlashXPQPQ
MiniMax M2.5PL
GPT-6 Luna
Muse Glimmer 30BPLPQ
PQ Playvox QMPL PlayvoxPQ first choicePQ named as an alternativePQ argued againstblank: not namedEach cell is one answer, Playvox QM on the left and Playvox on the right.

The direct prompt

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

Neither was the first choice, one was named

4 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Qwen 3.7 FlashCallMiner Eureka alternatives: Five9 Agent Connect, MaestroQA, NICE CXone Quality Management, Playvox
Kimi K2EvaluAgent alternatives: Playvox, ScorebuddyCX, Zendesk QA
GLM 4.7 FlashXAmplifAI, Balto alternatives: EvaluAgent, Playvox QM, Scorebuddy
Muse Glimmer 30BObserve.AI, Zendesk QA alternatives: EvaluAgent, Playvox, Scorebuddy

Neither was named

10 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Scorebuddy alternatives: Convin, EvaluAgent, Gong, Level AI
GPT-5.4 miniLevel AI alternatives: Calabrio ONE, Scorebuddy, Zendesk QA
Gemini 3.5 FlashZendesk QA alternatives: EvaluAgent, Level AI, MaestroQA
Perplexity SonarScorebuddy alternatives: AmplifAI, Balto, EvaluAgent, Level AI
Grok 4.1 FastEvaluAgent, Scorebuddy alternatives: Balto, Talkdesk QM, Zendesk QA
Mistral SmallEvaluAgent, Scorebuddy alternatives: Talkdesk
DeepSeek V4 FlashEvaluAgent alternatives: AmplifAI, Observe.AI, Scorebuddy, Talkdesk, Zendesk QA
Llama 4 MaverickEvaluAgent alternatives: GetApp, Scorebuddy
MiniMax M2.5Scorebuddy alternatives: EvaluAgent, Voxjar, 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
Playvox QM leads by four points.
Playvox QM4%#7 of 12
Playvox0%#– of 12
The full small business standing →
Mid-marketThe figures above
Playvox QM leads by four points.
Playvox QM6%#4 of 13
Playvox2%#7 of 13
The full mid-market standing →
Enterprise
Playvox QM is not named for this buyer.
Playvox QM—not named
Playvox0%#– of 14
The full enterprise standing →

What the models said about Playvox QM

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.

“Playvox or evaluagent are generally the most accessible and cost-effective dedicated options” Qwen 3.7 Flash · budget prompt · first choice
“I'd recommend starting with EvaluAgent or Playvox QM” GLM 4.7 FlashX · paraphrase prompt · first choice
“Top Pick: Playvox QM” DeepSeek V4 Flash · budget prompt · first choice

What the models said about Playvox

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.

“Playvox or Scorebuddy are likely your best bets” Kimi K2 · paraphrase prompt · first choice
“the best-in-class solutions (e.g., Playvox, Qualtrics, advanced EnsembleIQ styles) combine AI for 100% coverage with human calibration” DeepSeek V4 Flash · negative prompt · alternative
“Best for: Contact centers needing QA + workforce management + gamification in one platform” Kimi K2 · comparative prompt · alternative
Also compared
EvaluAgent vs Playvox QMEvaluAgent vs PlayvoxScorebuddy vs Playvox QMScorebuddy vs PlayvoxZendesk QA vs Playvox QMZendesk QA vs PlayvoxPlayvox QM vs CloudTalkPlayvox QM vs MaestroQAPlayvox QM vs Level AICloudTalk vs PlayvoxMaestroQA vs PlayvoxPlayvox vs Level AIAll twenty-eight head-to-head pages and the full Contact center QA standing

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.