# OnBoard vs Diligent: which do AI models recommend for board, September 2026

Finance AI Recommendation Index, September 2026 Edition, Board and investor reporting. Eight of twelve models named OnBoard first on the direct prompt; one named Diligent. Page: https://finance-ai-index.com/planning/board-and-investor-reporting/onboard-vs-diligent/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| OnBoard | 25% | #1 of 14 | 16% | 37 | 12 of 12 |
| Diligent | 2% | #6 of 14 | 71% | 17 | 9 of 12 |

## The direct prompt, model by model

- Claude Haiku 4.5: onboard first (first choices: OnBoard) (alternatives: Aprio Board Portal, Board Intelligence, HighRadius)
- GPT-5.4 mini: onboard first (first choices: OnBoard) (alternatives: Board Intelligence, BoardPro, Diligent Boards, Nasdaq Boardvantage)
- Gemini 3.5 Flash: onboard first (first choices: Mosaic, OnBoard) (alternatives: Board Intelligence, Cube, Datarails, Govenda, Jirav)
- Perplexity Sonar: onboard first (first choices: OnBoard) (alternatives: Board Intelligence, BoardEffect, Nasdaq Boardvantage)
- Grok 4.1 Fast: onboard first (first choices: OnBoard) (alternatives: AppDeck Board Portal, BoardPro, iBabs)
- DeepSeek V4 Flash: onboard first (first choices: OnBoard) (alternatives: AppDeck Board Portal, Fairview)
- GLM 4.7 FlashX: onboard first (first choices: Aprio Board Portal, OnBoard) (alternatives: AppDeck Board Portal, BoardEffect, Govenda, Nasdaq Boardvantage)
- MiniMax M2.5: onboard first (first choices: OnBoard) (alternatives: BoardEffect, Datarails, Planful)
- Qwen 3.7 Flash: diligent first (first choices: Diligent) (alternatives: Enboarder, Fello, OnBoard)
- Mistral Small: neither named (first choices: Nasdaq Boardvantage) (alternatives: BoardEffect, Datarails, Fairview)
- Llama 4 Maverick: neither named (first choices: Nasdaq Boardvantage) (alternatives: Datarails, Fairview)
- Kimi K2: neither named (first choices: Datarails) (alternatives: Cube, Planful)

## What the models said about OnBoard

- "OnBoard: Frequent gripes about agenda glitches, permission settings, navigation difficulties, and upload issues" (Grok 4.1 Fast, negative prompt, soft negative)
- "What to avoid on a tight budget... pricing is typically quote-based and often steeper" (GPT-5.4 mini, budget prompt, soft negative)
- "Good for board meetings, but be prepared for a learning curve and higher cost." (GLM 4.7 FlashX, negative prompt, soft negative)
- "OnBoard is identified as the "UX Leader" of 2026, using AI to automate board book creation and is best for mid-market corporations seeking rapid adoption" (Claude Haiku 4.5, direct prompt, first choice)
- "If you want a single recommendation: choose OnBoard for the best blend of mid-market fit, board functionality, and ease of adoption" (Perplexity Sonar, direct prompt, first choice)
- "OnBoard: Highly secure, incredibly intuitive, and widely considered the "sweet spot" for mid-market companies. Excellent mobile UX." (Gemini 3.5 Flash, scale prompt, first choice)

## What the models said about Diligent

- "Diligent ($15K\u201330K+/yr) and Board Intelligence are enterprise standard but far out of budget. Skip these" (DeepSeek V4 Flash, budget prompt, hard negative)
- "I would avoid Diligent, BoardEffect, and Boardvantage if budget is the main constraint" (Perplexity Sonar, budget prompt, hard negative)
- "Large public company: Diligent or Nasdaq Boardvantage (robust compliance, audit trails)" (MiniMax M2.5, comparative prompt, first choice)
- "Diligent is widely considered the gold standard." (Qwen 3.7 Flash, direct prompt, first choice)
- "Diligent is the titan of the industry. It is highly secure and customizable but can be very expensive and complex to implement." (Gemini 3.5 Flash, comparative prompt, alternative)

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. 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.