Qapita vs Shareworks
Four of twelve models named Qapita first on the direct prompt; one named Shareworks. Qapita was named by eleven of the twelve models and Shareworks by ten and both carry 26 labels, so the shares below are directly comparable.
Named in one category 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
QapitaFirst choices, of twelve modelsShareworks
Across every category in the September 2026 Edition, Qapita and Shareworks were named in the same answer thirty-three times, of the 67 answers naming Qapita and the 89 naming Shareworks. In those answers Shareworks took the first choice four times and Qapita 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 | | |
Qapita Shareworks first choice named as an alternative argued againstblank: not namedEach cell is one answer, Qapita on the left and Shareworks on the right.The direct prompt
Qapita first, Shareworks not the choice
4 of 12 modelsShareworks was named in the answer but not as the choice, or not at all.Shareworks first, Qapita not the choice
1 of 12 modelsQapita 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
2 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
Shareworks20%#2 of 9Qapita9%#3 of 9What the models said about Qapita
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Strong in Asia-Pacific, but not a leader in the US/EU.” DeepSeek V4 Flash · paraphrase prompt · soft negative
“Qapita and Carta are the most frequently recommended due to their scalability, global compliance features” Mistral Small · paraphrase prompt · first choice
“Qapita offers the best balance of full-lifecycle features and transparent pricing for mid-market scale” DeepSeek V4 Flash · direct prompt · first choice
“Carta and Qapita are the top recommendations based on scalability, feature set, and industry adoption” Mistral Small · direct prompt · first choice
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.