Sisense vs Qrvey
Five of fourteen models named Sisense first on the direct prompt; two named Qrvey. Sisense was named by fourteen of the fourteen models and Qrvey by twelve and Sisense carries 50 labels and Qrvey 25, so the shares are not directly comparable.
Named in one category this edition.
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 embedded analytics page.
By framing
SisenseFirst choices, of fourteen modelsQrvey
Across every category in the November 2026 Edition, Sisense and Qrvey were named in the same answer forty-five times, of the 140 answers naming Sisense and the 63 naming Qrvey. In those answers Qrvey took the first choice seven times and Sisense fifteen.
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 | | |
Sisense Qrvey first choice named as an alternative argued againstblank: not namedEach cell is one answer, Sisense on the left and Qrvey on the right.The direct prompt
Sisense first, Qrvey an alternative
5 of 14 modelsQrvey was named in the answer but not as the choice, or not at all.Qrvey first, Sisense an alternative
2 of 14 modelsSisense was named in the answer but not as the choice, or not at all.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.Neither was named
3 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
Sisense0%#12 of 13Qrvey0%#9 of 13Sisense11%#3 of 15Qrvey11%#4 of 15What the models said about Sisense
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Verdict: Exercise extreme caution, especially for sensitive data” Kimi K2 · negative prompt · hard negative
“Avoid high-end tools like Tableau ($75+/user), Sisense ($399+)” Grok 4.20 · budget prompt · hard negative
“Avoid platforms like Luzmo ($2,000+/month), Sisense” DeepSeek V4 Flash · budget prompt · hard negative
“Sisense is widely considered the premier embedded analytics platform, especially for complex data environments.” DeepSeek V4 Flash · comparative prompt · first choice
“If you're building a SaaS product: start with Sisense, ThoughtSpot Embedded, GoodData, or Qrvey” GPT-5.4 mini · comparative prompt · first choice
“The Best All-Rounder: Sisense ... widely considered the gold standard for mid-market companies” Qwen 3.7 Flash · direct prompt · first choice
What the models said about Qrvey
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“For a strictly limited budget, Qrvey or Embeddable are often recommended because their flat-rate/fixed pricing prevents unexpected costs” MiniMax M2.5 · budget prompt · first choice
“Need fast time-to-value, white-label customer portals and predictable flat-rate pricing? Start with Qrvey, Luzmo or Holistics.” Muse Glimmer 30B · direct prompt · first choice
“I recommend Qrvey as the top choice for embedded BI focused on customer-facing dashboards.” Grok 4.20 · 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.