Eight of fourteen models named Denodo first on the direct prompt; one named TIBCO Data Virtualization. Denodo was named by fourteen of the fourteen models and TIBCO Data Virtualization by twelve and Denodo carries 66 labels and TIBCO Data Virtualization 34, 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 data virtualization page.
Across every category in the October 2026 Edition, Denodo and TIBCO Data Virtualization were named in the same answer ninety-one times, of the 204 answers naming Denodo and the 96 naming TIBCO Data Virtualization. In those answers TIBCO Data Virtualization took the first choice one time and Denodo forty-two.
| 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 |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“What to Avoid on a Tight Budget ... Denodo | ~$180,000/year | Enterprise pricing is out of reach” DeepSeek V4 Flash · budget prompt · hard negative
“Premium enterprise - avoid for limited budgets” Kimi K2 · budget prompt · hard negative
“Highly proprietary enterprise suites like Denodo and TIBCO Data Virtualization can be strong technically, but they tend to come with premium pricing, operational complexity, and a risk of lock-in” GPT-5.4 mini · negative prompt · soft negative
“the strongest default choice is usually Denodo if you need broad connectivity, governance, and a mature enterprise-grade virtualization layer” Perplexity Sonar · direct prompt · first choice
“Choose Denodo if you need the most comprehensive, enterprise-grade data virtualization with the broadest source connectivity and governance.” DeepSeek V4 Flash · comparative prompt · first choice
“Denodo is a pure-play logical data fabric, while TIBCO emphasizes a semantic layer and SQL-based cross-source joins with pushdown.” Muse Glimmer 30B · comparative prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“one data science professional described their experience with a TIBCO-based platform as "so slow and shitty"” DeepSeek V4 Flash · negative prompt · hard negative
“Highly proprietary enterprise suites like Denodo and TIBCO Data Virtualization ... tend to come with premium pricing, operational complexity, and a risk of lock-in” GPT-5.4 mini · negative prompt · soft negative
“Older versions of platforms from Tibco, Cisco Data Virtualization, and legacy IBM offerings that haven't been updated for modern cloud workloads.” Kimi K2 · negative prompt · soft negative
“Choose Starburst if you are building a modern data stack centered around cloud object storage (S3/ADLS) and need fast SQL queries over massive datasets.” Qwen 3.7 Flash · comparative prompt · first choice
“Informatica and TIBCO are top choices due to their scalability and integration capabilities” Mistral Small · direct prompt · first choice
“Consider IBM Cloud Pak for Data or TIBCO Data Virtualization if your existing stack already leans toward those ecosystems” Perplexity Sonar · direct 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.