BoardEffect vs Nasdaq Boardvantage
Zero of fourteen models named BoardEffect first on the direct prompt; zero named Nasdaq Boardvantage. BoardEffect was named by thirteen of the fourteen models and Nasdaq Boardvantage by fourteen and BoardEffect carries 20 labels and Nasdaq Boardvantage 28, so the shares are not directly comparable.
Named in two categories this edition.
Named in two categories 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 board portals page.
By framing
BoardEffectFirst choices, of fourteen modelsNasdaq Boardvantage
Across every category in the October 2026 Edition, BoardEffect and Nasdaq Boardvantage were named in the same answer sixty-eight times, of the 104 answers naming BoardEffect and the 203 naming Nasdaq Boardvantage. In those answers Nasdaq Boardvantage took the first choice ten times and BoardEffect one.
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 | | |
BoardEffect Nasdaq Boardvantage first choice named as an alternative argued againstblank: not namedEach cell is one answer, BoardEffect on the left and Nasdaq Boardvantage on the right.The direct prompt
Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
9 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
BoardEffect2%#6 of 8Nasdaq Boardvantage0%#7 of 8BoardEffect2%#6 of 10Nasdaq Boardvantage2%#7 of 10Nasdaq Boardvantage19%#2 of 10BoardEffect2%#6 of 10What the models said about BoardEffect
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Only consider BoardEffect if your use case is more nonprofit-style or you want something simpler.” GPT-5.4 mini · paraphrase prompt · soft negative
“1. BoardEffect (now part of the Passageways family alongside OnBoard) - targets nonprofits and mid-sized organizations.” Llama 4 Maverick · paraphrase prompt · first choice
“BoardEffect is ideal for nonprofits and mid-sized organizations, offering a balance of features and ease of use.” Mistral Small · comparative prompt · alternative
What the models said about Nasdaq Boardvantage
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“Enterprise-grade pricing (~$20K–40K+/year) — designed for regulated public companies” Kimi K2 · direct prompt · hard negative
“avoiding the excessive price tags and bloated complexity of legacy enterprise portals built for massive public corporations (like Diligent or Nasdaq Boardvantage)” Gemini 3.5 Flash · paraphrase prompt · soft negative
“Public/enterprise companies → Diligent or Nasdaq Boardvantage (premium, security-first, enterprise scale)” DeepSeek V4 Flash · comparative prompt · first choice
“choose Diligent or Nasdaq Boardvantage for large, regulated enterprises needing deep compliance and analytics” Muse Glimmer 30B · comparative prompt · first choice
“For enterprise-scale governance, start with Diligent Boards and Nasdaq Boardvantage.” GPT-6 Luna · comparative 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.