EntityKeeper vs Commenda
Zero of fourteen models named EntityKeeper first on the direct prompt; two named Commenda. EntityKeeper was named by fourteen of the fourteen models and Commenda by eleven and EntityKeeper carries 40 labels and Commenda 19, so the shares are not directly comparable.
Named in one category this edition.
Named in four 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 entity management page.
By framing
EntityKeeperFirst choices, of fourteen modelsCommenda
Across every category in the October 2026 Edition, EntityKeeper and Commenda were named in the same answer eighteen times, of the 103 answers naming EntityKeeper and the 110 naming Commenda. In those answers Commenda took the first choice one time and EntityKeeper four.
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 | | | |
EntityKeeper Commenda first choice named as an alternative argued againstblank: not namedEach cell is one answer, EntityKeeper on the left and Commenda on the right.The direct prompt
Commenda first, EntityKeeper not the choice
2 of 14 modelsEntityKeeper was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
8 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
4 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
EntityKeeper23%#1 of 8Commenda2%#– of 8EntityKeeper6%#4 of 11Commenda4%#6 of 11EntityKeeper0%#9 of 9Commenda0%#– of 9What the models said about EntityKeeper
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“I'd skip EntityKeeper on a tight budget: its published annual plans start at $5,000” GPT-6 Luna · budget prompt · hard negative
“Avoid giants like EntityKeeper or Diligent unless you are prepared to pay $5,000+/year” Qwen 3.7 Flash · budget prompt · hard negative
“"Legacy/Standalone Entity Databases (e.g., Basic Spreadsheets or Early CRMs like Generic \"EntityKeeper\" for Scale-Up Beyond Small Portfolios)"” Muse Glimmer 30B · negative prompt · soft negative
“For the most budget-conscious option, EntityKeeper's free plan is a strong starting point.” Mistral Small · budget prompt · first choice
“🥇 Top Pick: EntityKeeper (for Entity Management)” DeepSeek V4 Flash · paraphrase prompt · first choice
“EntityKeeper is the tool I'd recommend first” MiniMax M2.5 · paraphrase prompt · first choice
What the models said about Commenda
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Reported issues include: Complex interface causing confusion - Tax glitches and exemption problems” Kimi K2 · negative prompt · hard negative
“such as Commenda or ContractZen... Commenda - designed for mid-market companies with 5-30 entities across 8+ countries.” Llama 4 Maverick · direct prompt · first choice
“For most mid-market companies, Commenda and Athennian are frequently cited as top choices.” Claude Haiku 4.5 · direct 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.