Paddle vs Dodo Payments
Six of fourteen models named Paddle first on the direct prompt; zero named Dodo Payments. Paddle was named by fourteen of the fourteen models and Dodo Payments by eleven and Paddle carries 65 labels and Dodo Payments 17, so the shares are not directly comparable.
Named in six categories this edition.
Named in four categories this edition.
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 merchants of record page.
By framing
PaddleFirst choices, of fourteen modelsDodo Payments
Across every category in the October 2026 Edition, Paddle and Dodo Payments were named in the same answer twenty-seven times, of the 214 answers naming Paddle and the 48 naming Dodo Payments. In those answers Dodo Payments took the first choice four times and Paddle 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 | | | | | | |
| GPT-6 Luna | | | | | | |
| Muse Glimmer 30B | | | |
Paddle Dodo Payments first choice named as an alternative argued againstblank: not namedEach cell is one answer, Paddle on the left and Dodo Payments on the right.The direct prompt
Paddle first, Dodo Payments not the choice
6 of 14 modelsDodo Payments was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
8 of 14 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
Paddle29%#1 of 10Dodo Payments6%#5 of 10Paddle28%#1 of 10Dodo Payments5%#6 of 10Paddle33%#1 of 7Dodo Payments0%#– of 7What the models said about Paddle
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Paddle abruptly terminated my account without offering a clear rationale... they continue to withhold our money.” Muse Glimmer 30B · negative prompt · hard negative
“Why to be cautious: While Paddle is a highly legitimate and capable MoR, this regulatory crackdown forced them to put their compliance... into overdrive.” Gemini 3.5 Flash · negative prompt · soft negative
“If you are a pure-play SaaS or digital service provider and want to minimize the administrative burden of tax filing while scaling rapidly, Paddle is the market favorite.” Qwen 3.7 Flash · direct prompt · first choice
“For pure-play B2B SaaS (Hybrid Sales + PLG): Look closely at Paddle. They have heavily invested in moving up-market to serve mid-market and enterprise SaaS companies” Gemini 3.5 Flash · scale prompt · first choice
“Paddle: The market leader for SaaS and digital products. Strong compliance framework and massive partner network” Qwen 3.7 Flash · scale prompt · first choice
What the models said about Dodo Payments
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Lowest published headline rate: Dodo Payments... Try Dodo if your priority is the lowest published fee” GPT-6 Luna · budget prompt · first choice
“Dodo Payments looks like the strongest low-budget option at 4% + 40¢ with no monthly fees” Perplexity Sonar · budget prompt · first choice
“the best merchant of record for a company with a limited budget is Dodo Payments” GLM 4.7 FlashX · 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.