# Ansarada vs Datasite Diligence: which do AI models recommend for virtual data rooms, October 2026

Finance AI Recommendation Index, October 2026 Edition, Virtual data rooms. Zero of fourteen models named Ansarada first on the direct prompt; zero named Datasite Diligence. Page: https://finance-ai-index.com/corporate/virtual-data-rooms/ansarada-vs-datasite-diligence/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Ansarada | 3% | #7 of 15 | 10% | 21 | 11 of 14 |
| Datasite Diligence | 3% | #8 of 15 | 47% | 53 | 14 of 14 |

## The direct prompt, model by model

- GPT-5.4 mini: neither first, one named (first choices: iDeals Virtual Data Room) (alternatives: Clinked, Datasite Diligence, Firmex, SecureDocs)
- Perplexity Sonar: neither first, one named (first choices: Firmex) (alternatives: Datasite Diligence, Papermark, SecureDocs, iDeals)
- GPT-6 Luna: neither first, one named (first choices: Firmex) (alternatives: Datasite Diligence, DealRoom, Intralinks, iDeals)
- Claude Haiku 4.5: neither named (first choices: iDeals) (alternatives: CapLinked, Firmex)
- Gemini 3.5 Flash: neither named (first choices: iDeals) (alternatives: CapLinked, DealRoom, Firmex, ShareVault)
- Grok 4.1 Fast: neither named (first choices: Firmex, iDeals) (alternatives: FirmRoom, SecureDocs, ShareVault)
- Mistral Small: neither named (first choices: Firmex, iDeals) (alternatives: FirmRoom)
- DeepSeek V4 Flash: neither named (first choices: iDeals) (alternatives: Firmex, SecureDocs)
- Llama 4 Maverick: neither named
- Qwen 3.7 Flash: neither named (first choices: FirmRoom, Firmex) (alternatives: iDeals)
- Kimi K2: neither named (first choices: iDeals) (alternatives: DealRoom, FirmRoom, Firmex)
- GLM 4.7 FlashX: neither named (first choices: iDeals Virtual Data Room) (alternatives: Clinked, DealRoom, Firmex)
- MiniMax M2.5: neither named (first choices: iDeals) (alternatives: FirmRoom, Firmex)
- Muse Glimmer 30B: neither named (first choices: iDeals) (alternatives: DealRoom, FirmRoom, Firmex)

## What the models said about Ansarada

- "One concrete example to examine carefully: Ansarada’s storage-overage terms. ... That’s a pricing and contract caution—not a claim that the product is unsafe." (GPT-6 Luna, negative prompt, soft negative)
- "Watch out for "peak usage" storage clauses (such as those in Ansarada's pricing)" (Gemini 3.5 Flash, negative prompt, soft negative)
- "Modern, Scalable Mid-Market (Strongest fit for a 500-person firm): Examples: iDeals, Firmex, Ansarada." (Gemini 3.5 Flash, scale prompt, first choice)
- "my default recommendation would be Ansarada" (GPT-5.4 mini, paraphrase prompt, first choice)
- "Ansarada is noted for "AI-supported deal workflows, M&A, capital raising, infrastructure procurement, and structured diligence processes"" (Muse Glimmer 30B, comparative prompt, alternative)

## What the models said about Datasite Diligence

- "What to Avoid ... Enterprise providers like Datasite or Intralinks: $5,000–$50,000+ per deal" (Kimi K2, budget prompt, hard negative)
- "Avoid legacy per-page pricing (Intralinks, Datasite) unless doing large cross-border deals." (Kimi K2, direct prompt, hard negative)
- "Avoid legacy providers like Datasite and Intralinks" (DeepSeek V4 Flash, budget prompt, hard negative)
- "Built around M&A workflows, with AI-assisted document search and review, redaction, Q&A, and deal tools. A strong candidate when handling a large room or many bidders." (GPT-6 Luna, comparative prompt, first choice)
- "Choose Intralinks/Datasite if: You're working on a large M&A deal where your counterparty (investment bank) mandates it" (Kimi K2, comparative prompt, first choice)
- "Best for large enterprise M&A and complex, high-value transactions... considered the enterprise standard for M&A" (Mistral Small, comparative prompt, first choice)

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. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.