Sisense vs Embeddable
Five of fourteen models named Sisense first on the direct prompt; zero named Embeddable. Sisense was named by fourteen of the fourteen models and Embeddable by eleven and Sisense carries 50 labels and Embeddable 27, 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 modelsEmbeddable
Across every category in the November 2026 Edition, Sisense and Embeddable were named in the same answer fifty-three times, of the 140 answers naming Sisense and the 82 naming Embeddable. In those answers Embeddable took the first choice two times and Sisense nine.
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 Embeddable first choice named as an alternative argued againstblank: not namedEach cell is one answer, Sisense on the left and Embeddable on the right.The direct prompt
Sisense first, Embeddable not the choice
5 of 14 modelsEmbeddable 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
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 13Embeddable0%#8 of 13Sisense30%#1 of 15Embeddable15%#2 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 Embeddable
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Embeddable (custom/flat but higher for unlimited use)” Grok 4.20 · budget prompt · hard negative
“Qrvey or Embeddable are often recommended because their flat-rate/fixed pricing prevents unexpected costs as usage grows” MiniMax M2.5 · budget prompt · first choice
“Embeddable is the top recommendation due to its focus on customer-facing analytics, security, and scalable pricing” Mistral Small · paraphrase prompt · first choice
“Consider embedded-native options like Embeddable, Plotly Dash, Retool, or Mode Analytics for better integration” Grok 4.20 · negative 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.