Trino vs Starburst
Zero of fourteen models named Trino first on the direct prompt; zero named Starburst. Trino was named by ten of the fourteen models and Starburst by thirteen and Trino carries 18 labels and Starburst 32, so the shares are not directly comparable.
Named in two categories 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 data virtualization page.
By framing
TrinoFirst choices, of fourteen modelsStarburst
Across every category in the October 2026 Edition, Trino and Starburst were named in the same answer nineteen times, of the 54 answers naming Trino and the 82 naming Starburst. In those answers Starburst took the first choice one time and Trino 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 | | |
Trino Starburst first choice named as an alternative argued againstblank: not namedEach cell is one answer, Trino on the left and Starburst on the right.The direct prompt
Neither was the first choice, one was named
2 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
Trino0%#– of 8Starburst0%#6 of 8What the models said about Trino
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Trino/Starburst-style solutions can work well, but they may be the wrong choice if your use case needs heavy governance, complex business semantics” GPT-5.4 mini · negative prompt · soft negative
“called out as open-source platforms with no certifications, which makes them risky in regulated environments” Perplexity Sonar · negative prompt · soft negative
“Be cautious if you lack the team to own connectors, upgrades, security, and query tuning.” GPT-6 Luna · negative prompt · soft negative
“Start with Trino if you have technical resources - it's the most widely adopted open-source option” Kimi K2 · budget prompt · first choice
“Trino is the best data virtualization platform for a company with a limited budget” MiniMax M2.5 · budget prompt · first choice
“Trino if you have technical talent and want to avoid licensing costs entirely” GLM 4.7 FlashX · budget prompt · first choice
What the models said about Starburst
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Starburst | Contact sales (commercial) | While Trino-based, paid plans get expensive” DeepSeek V4 Flash · budget prompt · hard negative
“Starburst | Modern, Trino-based, cloud-native | Newer, smaller ecosystem” Kimi K2 · scale prompt · soft negative
“Leaders like Denodo ... Starburst consistently rank high for enterprise use, with strong governance and hybrid support.” Grok 4.1 Fast · negative prompt · first choice
“Start with Starburst Enterprise or Dremio Cloud... Starburst is the more "pure" federated query engine” Muse Glimmer 30B · paraphrase prompt · first choice
“I recommend Starburst (using the open-source Trino engine) as the primary choice” Qwen 3.7 Flash · 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.