Finance AI Index
Index Equity and corporate Cap table management › Ledgy vs Fidelity Private Shares
Cap table management · September 2026 Edition

Ledgy vs Fidelity Private Shares

Zero of twelve models named Ledgy first on the direct prompt; zero named Fidelity Private Shares. Ledgy was named by twelve of the twelve models and Fidelity Private Shares by nine and Ledgy carries 36 labels and Fidelity Private Shares 14, so the shares are not directly comparable.

Ledgy

accepted challenger

Named in two categories this edition.

Fidelity Private Shares

accepted challenger

Named in one category this edition.

First-choice share3%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate6%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#6#8A position in a field of 8; printed, not drawn.
Labels3614A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Ledgy reading right to left. Rank and label count are printed, not drawn.Carta was named alongside these two in eleven of the twelve direct answers. Carta vs Ledgy · Carta vs Fidelity Private Shares · Qapita vs Ledgy

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 cap table 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.
LedgyFirst choices, of twelve modelsFidelity Private Shares
Direct00
Paraphrase10
Comparative00
Budget-constrained011 against Ledgy
Scale-constrained10
Negative001 against Ledgy
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.

Across every category in the September 2026 Edition, Ledgy and Fidelity Private Shares were named in the same answer fourteen times, of the 104 answers naming Ledgy and the 27 naming Fidelity Private Shares. In those answers Fidelity Private Shares took the first choice zero times and Ledgy zero.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Ledgy and Fidelity Private Shares 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
Ledgy Fidelity Private Shares first choice named as an alternative argued againstblank: not namedEach cell is one answer, Ledgy on the left and Fidelity Private Shares on the right.

The direct prompt

The plain question, one answer per model, grouped by where Ledgy and Fidelity Private Shares stood in it.

Neither was the first choice, one was named

6 of 12 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniCarta alternatives: Eqvista, Ledgy, Shareworks
Gemini 3.5 FlashCarta alternatives: ClosingWell, Ledgy, Pulley, Qapita
Grok 4.1 FastCarta alternatives: Ledgy, Pulley
DeepSeek V4 FlashCarta, Qapita alternatives: Ledgy
Kimi K2Carta alternatives: Cake Equity, Eqvista, Ledgy, Pulley
MiniMax M2.5Carta, Shareworks alternatives: EquityZen, Ledgy

Neither was named

6 of 12 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Carta alternatives: Cake Equity, Pulley, Qapita
Perplexity SonarCarta alternatives: Qapita
Mistral SmallCarta, Qapita
Llama 4 MaverickCarta, Pulley, Qapita
Qwen 3.7 FlashCarta alternatives: Pulley, Qapita
GLM 4.7 FlashXQapita alternatives: Cake Equity, Capshare, Mantle, Pulley

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
Level: the same share of first choices.
Ledgy0%#6 of 6
Fidelity Private Shares0%#– of 6
The full small business standing →
Mid-marketThe figures above
Ledgy leads by two points.
Ledgy3%#6 of 8
Fidelity Private Shares2%#8 of 8
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Ledgy0%#8 of 9
Fidelity Private Shares0%#– of 9
The full enterprise standing →

What the models said about Ledgy

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

“Avoid for tight budgets: ... Ledgy (€900/year+)” Grok 4.1 Fast · budget prompt · hard negative
“many reviews note that its scenario modeling lacks depth for complex liquidation preferences” DeepSeek V4 Flash · negative prompt · soft negative
“I'd suggest Carta if you're in the US or Ledgy if you're international/European” MiniMax M2.5 · paraphrase prompt · first choice
“Shortlist 3 (e.g., Carta, Pulley, Ledgy) based on your geography/ESOP needs.” Grok 4.1 Fast · scale prompt · first choice
“If your company is headquartered in Europe, the UK, or APAC, these platforms are highly competitive” Gemini 3.5 Flash · scale prompt · alternative

What the models said about Fidelity Private Shares

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

“starting with a free tier (Carta Launch or Fidelity Private Shares)” DeepSeek V4 Flash · budget prompt · first choice
“Offers secure equity management with strong support and investor access for small and mid-market companies.” Llama 4 Maverick · paraphrase prompt · alternative
“Fidelity Private Shares (Free for <25 stakeholders and <$1M raised): Bank-backed reliability” Grok 4.1 Fast · 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.