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Index › Equity and corporate › Entity management › Filejet vs Discern
Entity management · October 2026 Edition

Filejet vs Discern

One of fourteen models named Filejet first on the direct prompt; zero named Discern. Filejet was named by eleven of the fourteen models and Discern by seven and Filejet carries 17 labels and Discern 11, so the shares are not directly comparable.

Filejet

accepted challenger

Named in one category this edition.

Discern

accepted challenger

Named in one category this edition.

First-choice share6%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#8A position in a field of 11; printed, not drawn.
Labels1711A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Filejet reading right to left. Rank and label count are printed, not drawn.Athennian was named alongside these two in eleven of the fourteen direct answers. Athennian vs Filejet · Athennian vs Discern · ContractZen vs Filejet

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
FilejetFirst choices, of fourteen modelsDiscern
Direct10
Paraphrase00
Comparative00
Budget-constrained21
Scale-constrained00
Negative00
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Across every category in the October 2026 Edition, Filejet and Discern were named in the same answer thirteen times, of the 61 answers naming Filejet and the 28 naming Discern. In those answers Discern took the first choice one time and Filejet one.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Filejet and Discern 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
GPT-6 Luna
Muse Glimmer 30B
Filejet Discern first choice named as an alternative argued againstblank: not namedEach cell is one answer, Filejet on the left and Discern on the right.

The direct prompt

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

Filejet first, Discern an alternative

1 of 14 modelsDiscern was named in the answer but not as the choice, or not at all.
Mistral SmallAthennian, Filejet alternatives: Commenda, Diligent Entities, Discern

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.
Grok 4.1 FastAthennian alternatives: Commenda, EntityKeeper, Filejet
DeepSeek V4 FlashAthennian alternatives: Commenda, EntityKeeper, Filejet
Kimi K2Athennian alternatives: Filejet, NEWTON
GLM 4.7 FlashXAthennian alternatives: Diligent Entities, EntityKeeper, Filejet
Muse Glimmer 30BAthennian alternatives: CSC Entity Management, CT Corporation Entity Management, Commenda, Discern

Neither was named

8 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Athennian, Commenda alternatives: Klea
GPT-5.4 miniDiligent Entities alternatives: Athennian, CSC Entity Management
Gemini 3.5 FlashAthennian alternatives: ContractZen, Diliforce, EntityKeeper, MinuteBox
Perplexity SonarDiligent Entities alternatives: Commenda, GateWay Entities
Llama 4 MaverickCommenda, ContractZen alternatives: NEWTON
Qwen 3.7 FlashLegally alternatives: Diligent Boards, Docuum, Onspring
MiniMax M2.5Athennian, Diligent Entities alternatives: Eqvista, Harbor Compliance
GPT-6 LunaAthennian alternatives: CSC Entity Management, EntityKeeper

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
Filejet leads by thirteen points.
Filejet17%#2 of 8
Discern4%#– of 8
The full small business standing →
Mid-marketThe figures above
Filejet leads by four points.
Filejet6%#5 of 11
Discern2%#8 of 11
The full mid-market standing →
Enterprise
Filejet leads by two points.
Filejet2%#5 of 9
Discern0%#– of 9
The full enterprise standing →

What the models said about Filejet

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

“Filejet is a strong candidate if you want entity management focused on corporate records, compliance, and filings” GPT-5.4 mini · budget prompt · first choice
“Filejet stands out as the best legal entity management software for a company with a limited budget” Grok 4.1 Fast · budget prompt · first choice
“Athennian and Filejet are the top-rated options” Mistral Small · direct prompt · first choice

What the models said about Discern

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

“For most companies with a limited budget, Discern offers the most cost-effective entry point” DeepSeek V4 Flash · budget prompt · first choice
“Diligent Entities and Discern are also excellent choices, especially for companies with more complex compliance needs” Mistral Small · direct prompt · alternative
“Discern and CoverPin offer excellent value for money with transparent pricing and robust functionality.” Mistral Small · budget prompt · alternative
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.