Vena vs Datarails
Two of twelve models named Vena first on the direct prompt; one named Datarails. Both were named by all twelve models and both carry 45 labels, so the shares below are directly comparable.
Named in ten categories this edition.
Named in ten categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 FP&A platforms page.
By framing
VenaFirst choices, of twelve modelsDatarails
Across every category in the September 2026 Edition, Vena and Datarails were named in the same answer 158 times, of the 348 answers naming Vena and the 273 naming Datarails. In those answers Datarails took the first choice fourteen times and Vena 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 4.1 Fast | | | | | | |
| Mistral Small | | | | | ||
| DeepSeek V4 Flash | | | | | ||
| Llama 4 Maverick | | |||||
| Qwen 3.7 Flash | | | | | ||
| Kimi K2 | | | ||||
| GLM 4.7 FlashX | | | | | ||
| MiniMax M2.5 | | |
Vena Datarails first choice named as an alternative argued againstblank: not namedEach cell is one answer, Vena on the left and Datarails on the right.The direct prompt
Vena first, Datarails an alternative
2 of 12 modelsDatarails was named in the answer but not as the choice, or not at all.Datarails first, Vena an alternative
1 of 12 modelsVena was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
6 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 of 12 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
Vena2%#7 of 13Datarails2%#5 of 13Vena0%#12 of 12Datarails0%#– of 12What the models said about Vena
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“"would rather eat a bullet than use it again." ... Anaplan and Vena are the two with the most consistent negative sentiment” DeepSeek V4 Flash · negative prompt · hard negative
“If data sovereignty and verified security audits are non-negotiable for you, this is a major red flag.” Qwen 3.7 Flash · negative prompt · hard negative
“Avoid: Vena (ongoing performance, learning curve, support, and security concerns)” GLM 4.7 FlashX · negative prompt · hard negative
“Go with Vena if your finance team wants to continue modeling in Excel but needs to eliminate spreadsheet errors and automate data ingestion from your ERP.” Gemini 3.5 Flash · paraphrase prompt · first choice
“Choose Vena or Datarails if you have a small to mid-sized finance team that wants to retain the comfort of using Excel” Qwen 3.7 Flash · comparative prompt · first choice
What the models said about Datarails
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“The Pitfall (specifically Datarails): ... its pricing has risen significantly... the ROI may not add up.” Gemini 3.5 Flash · negative prompt · hard negative
“Avoid pricier ones like Jirav (~$10K/year), Cube/Datarails ($1K+/month)” Grok 4.1 Fast · budget prompt · hard negative
“Datarails is often the strongest all-around pick — Excel-based, strong consolidation and variance analysis, faster to ROI” DeepSeek V4 Flash · direct prompt · first choice
“Start with Datarails or Vena if your team is comfortable in Excel and wants minimal disruption” Kimi K2 · 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.