Finance AI Index
Index Equity and corporate Entity management › Commenda vs Diligent
Entity management · September 2026 Edition

Commenda vs Diligent

Four of twelve models named Commenda first on the direct prompt; zero named Diligent. Commenda was named by nine of the twelve models and Diligent by eight and Commenda carries 16 labels and Diligent 11, so the shares are not directly comparable.

Commenda

accepted challenger

Named in two categories this edition.

Diligent

criticized challenger

Named in two categories this edition.

First-choice share16%3%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate6%64%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#8A position in a field of 12; printed, not drawn.
Labels1611A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Commenda reading right to left. Rank and label count are printed, not drawn.Diligent Entities was named alongside these two in eight of the twelve direct answers. Athennian vs Commenda · Athennian vs Diligent · EntityKeeper vs Commenda

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 entity management page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
CommendaFirst choices, of twelve modelsDiligent
Direct40
Paraphrase014 against Diligent
Comparative10
Budget-constrained201 against Diligent
Scale-constrained001 against Diligent
Negative001 against Commenda · 1 against Diligent
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Commenda and Diligent stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Commenda Diligent first choice named as an alternative argued againstblank: not namedEach cell is one answer, Commenda on the left and Diligent on the right.

The direct prompt

The plain question, one answer per model, grouped by where Commenda and Diligent stood in it.

Commenda first, Diligent not the choice

4 of 12 modelsDiligent was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Commenda alternatives: Athennian, CT Corporation Entity Management, Diligent Entities
Perplexity SonarCommenda alternatives: Computershare GEMS, Fides
Qwen 3.7 FlashCommenda alternatives: Diligent Entities, LegalX
MiniMax M2.5Commenda alternatives: Athennian, Diligent Entities, GateWay Entities

Neither was the first choice, one was named

4 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Mistral SmallContractZen alternatives: Commenda, GateWay Entities
DeepSeek V4 FlashAthennian alternatives: CSC Entity Management, CT Corporation Entity Management, Commenda, Diligent Entities, EntityKeeper
Kimi K2Filejet alternatives: Athennian, Commenda, Diligent Entities
GLM 4.7 FlashXAthennian alternatives: CSC Entity Management, Commenda, Diligent Entities, EntityKeeper

Neither was named

4 of 12 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniDiligent Entities alternatives: Harbor Compliance
Gemini 3.5 FlashAthennian alternatives: EntityKeeper, Filejet, MinuteBox
Grok 4.1 FastAthennian alternatives: Carta, Diligent Entities, Pulley
Llama 4 MaverickOcorian alternatives: Coverpin, F6S

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Commenda leads by two points.
Commenda2%#– of 12
Diligent0%#12 of 12
The full small business standing →
Mid-marketThe figures above
Commenda leads by thirteen points.
Commenda16%#3 of 12
Diligent3%#8 of 12
The full mid-market standing →
Enterprise
The order flips: Diligent leads at enterprise.
Diligent9%#– of 8
Commenda2%#– of 8
The full enterprise standing →

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.

“Other tools like Commenda have occasional gripes (complex UI, tax glitches, integrations), but positives outweigh them.” Grok 4.1 Fast · negative prompt · soft negative
“Often cited as a leader for international companies. It combines entity management with AI-driven compliance monitoring and incorporates entities in 70+ countries.” Qwen 3.7 Flash · comparative prompt · first choice
“Commenda is a strong budget-friendly contender that balances functionality with affordability” Claude Haiku 4.5 · budget prompt · first choice

What the models said about Diligent

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.

“Legacy Systems (e.g., Diligent, CSC Entity Management): Often highly robust ... but can feel clunky, require heavy training, have longer implementation times, and carry higher price tags.” Gemini 3.5 Flash · scale prompt · soft negative
“Many legacy systems (like Diligent, Mitratech, or CSC) are designed for large multinationals, hide their pricing ... often start at tens of thousands of dollars per year.” Gemini 3.5 Flash · budget prompt · soft negative
“## Top Recommendation: Diligent” GLM 4.7 FlashX · paraphrase prompt · first choice
Also compared

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.