Luzmo vs GoodData
One of fourteen models named Luzmo first on the direct prompt; zero named GoodData. Luzmo was named by eleven of the fourteen models and GoodData by seven and Luzmo carries 29 labels and GoodData 15, so the shares are not directly comparable.
Named in one category this edition.
Named in two 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 embedded analytics page.
By framing
LuzmoFirst choices, of fourteen modelsGoodData
Across every category in the October 2026 Edition, Luzmo and GoodData were named in the same answer twenty-seven times, of the 84 answers naming Luzmo and the 60 naming GoodData. In those answers GoodData took the first choice six times and Luzmo seven.
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 | | |
Luzmo GoodData first choice named as an alternative argued againstblank: not namedEach cell is one answer, Luzmo on the left and GoodData on the right.The direct prompt
Luzmo first, GoodData an alternative
1 of 14 modelsGoodData was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
1 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
12 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
GoodData16%#1 of 15Luzmo0%#13 of 15What the models said about Luzmo
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“avoid per-viewer pricing like Metabase Pro or Luzmo if user base grows fast” Grok 4.1 Fast · budget prompt · soft negative
“I'd recommend Luzmo or Explo if your top priority is fast, white-labeled dashboard embedding with minimal engineering overhead.” Perplexity Sonar · paraphrase prompt · first choice
“Dedicated embedded analytics platforms (like Luzmo, Toucan, Reveal, Cube) were built from the ground up for embedding.” DeepSeek V4 Flash · negative prompt · first choice
“If you want to get it done in a week with a beautiful, fully white-labeled UX and zero developer headache: Go with Luzmo.” Gemini 3.5 Flash · paraphrase prompt · first choice
What the models said about GoodData
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoid: Looker, Domo, ThoughtSpot, and GoodData if budget is your primary constraint” DeepSeek V4 Flash · budget prompt · hard negative
“Don't use: GoodData (complexity, pricing, proprietary language)” DeepSeek V4 Flash · negative prompt · hard negative
“Steep learning curve (proprietary MAQL language); per-workspace pricing scales poorly for multi-tenant” Grok 4.1 Fast · negative prompt · soft negative
“Choose GoodData/Looker if you are building a software product (SaaS) and need the analytics to look like part of the app itself.” Qwen 3.7 Flash · comparative prompt · first choice
“platforms like Sisense, GoodData, ThoughtSpot, and Explo were purpose-built for that use case” DeepSeek V4 Flash · comparative prompt · first choice
“Top for Scalability: GoodData, ThoughtSpot (strong multi-tenant/AI).” Grok 4.1 Fast · scale 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.