Carta vs Cake Equity
Eleven of twelve models named Carta first on the direct prompt; zero named Cake Equity. Carta was named by twelve of the twelve models and Cake Equity by eleven and Carta carries 65 labels and Cake Equity 38, so the shares are not directly comparable.
Named in three 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
CartaFirst choices, of twelve modelsCake Equity
Across every category in the September 2026 Edition, Carta and Cake Equity were named in the same answer eighty-eight times, of the 230 answers naming Carta and the 94 naming Cake Equity. In those answers Cake Equity took the first choice fifteen times and Carta thirty-six.
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 | | | | | |
Carta Cake Equity first choice named as an alternative argued againstblank: not namedEach cell is one answer, Carta on the left and Cake Equity on the right.The direct prompt
Carta first, Cake Equity an alternative
11 of 12 modelsCake Equity was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
1 of 12 modelsThe answer put something else first and named one of the two as an alternative.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
Cake Equity20%#2 of 6Carta16%#4 of 6Carta44%#1 of 8Cake Equity7%#5 of 8What the models said about Carta
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Skip enterprise tools like Carta's paid plans (Build/Grow can run $3,000–$12,000/year” DeepSeek V4 Flash · budget prompt · hard negative
“Several industry voices explicitly recommend *not* working with Carta.” Kimi K2 · negative prompt · hard negative
“Avoid Carta for core trust reasons” DeepSeek V4 Flash · negative prompt · hard negative
“Carta (Scale/Enterprise Tier): The institutional default... highly capable for late-stage secondary transactions, but it is also the most expensive and has faced scrutiny over aggressive pricing hikes.” Gemini 3.5 Flash · scale prompt · first choice
“The industry standard... the platform that virtually every institutional investor, auditor, and law firm recognizes as the *authoritative* equity record.” DeepSeek V4 Flash · comparative prompt · first choice
“Institutional trust & full compliance depth | Carta... you'll likely end up there for credibility and compliance depth despite the cost” DeepSeek V4 Flash · direct prompt · first choice
What the models said about Cake Equity
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Qapita (for its global, audit‑ready, mid‑market focus) and Cake Equity (for intuitive UX and transparent pricing) are both strong starting points” GLM 4.7 FlashX · paraphrase prompt · first choice
“The best cap table management software for a company with a limited budget is Cake Equity, which offers a free starting point” Llama 4 Maverick · budget prompt · first choice
“the best cap table management software is usually Cake Equity if you want a free starter tier” Perplexity Sonar · budget prompt · first choice
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.