Ledgy vs Shareworks
Zero of twelve models named Ledgy first on the direct prompt; one named Shareworks. Ledgy was named by twelve of the twelve models and Shareworks by ten and Ledgy carries 36 labels and Shareworks 26, so the shares are not directly comparable.
Named in two categories this edition.
Named in one category this edition.
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
LedgyFirst choices, of twelve modelsShareworks
Across every category in the September 2026 Edition, Ledgy and Shareworks were named in the same answer fifty-nine times, of the 104 answers naming Ledgy and the 89 naming Shareworks. In those answers Shareworks took the first choice ten times and Ledgy 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 | | | |
Ledgy Shareworks first choice named as an alternative argued againstblank: not namedEach cell is one answer, Ledgy on the left and Shareworks on the right.The direct prompt
Shareworks first, Ledgy an alternative
1 of 12 modelsLedgy was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
5 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
6 of 12 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
Ledgy0%#6 of 6Shareworks0%#– of 6Ledgy3%#6 of 8Shareworks3%#7 of 8What 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 Shareworks
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“treat any enterprise-level platform (Shareworks, Morgan Stanley) as a contract trap for early-stage companies” DeepSeek V4 Flash · negative prompt · hard negative
“and Shareworks, which are overkill and overpriced for a budget-conscious company” DeepSeek V4 Flash · budget prompt · hard negative
“### 3. The One to Skip (with a caveat): Shareworks by Morgan Stanley” DeepSeek V4 Flash · paraphrase prompt · hard negative
“Carta or Shareworks are typically the most balanced choices” MiniMax M2.5 · direct prompt · first choice
“Strong for your profile, but quote-only pricing” DeepSeek V4 Flash · scale prompt · first choice
“The gold standard if your company is actively planning an IPO within the next 12 to 24 months... generally considered heavy and slow” Gemini 3.5 Flash · scale prompt · alternative
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.