Mercury vs Rho
One of fourteen models named Mercury first on the direct prompt; two named Rho. Mercury was named by fourteen of the fourteen models and Rho by ten and Mercury carries 44 labels and Rho 13, so the shares are not directly comparable.
Named in seven categories this edition.
Named in five 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 business banking page.
By framing
MercuryFirst choices, of fourteen modelsRho
Across every category in the October 2026 Edition, Mercury and Rho were named in the same answer forty-four times, of the 194 answers naming Mercury and the 91 naming Rho. In those answers Rho took the first choice nine times and Mercury thirteen.
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 | | |
Mercury Rho first choice named as an alternative argued againstblank: not namedEach cell is one answer, Mercury on the left and Rho on the right.The direct prompt
Mercury first, Rho not the choice
1 of 14 modelsRho was named in the answer but not as the choice, or not at all.Rho first, Mercury an alternative
2 of 14 modelsMercury was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
8 of 14 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
Mercury35%#1 of 12Rho2%#8 of 12Rho19%#1 of 11Mercury8%#5 of 11What the models said about Mercury
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Mercury, Relay, and BlueVine draw approximately four times the complaints of other business banking providers” MiniMax M2.5 · negative prompt · hard negative
“Highest rate of negative reviews among business banks (15.4/month), with ~41% citing sudden account closures” Grok 4.1 Fast · negative prompt · hard negative
“Accounts are being closed or frozen at an unusually high rate ... Highest-Caution Fintech Providers” DeepSeek V4 Flash · negative prompt · hard negative
“For a US startup with a tiny budget: Novo or Mercury (both completely free with $0 minimums and excellent features).” DeepSeek V4 Flash · budget prompt · first choice
“Bluevine or Mercury currently offer the best combination of zero fees, features, and accessibility” MiniMax M2.5 · budget prompt · first choice
“Mercury has effectively become the default operating account for mid-market B2B companies.” GLM 4.7 FlashX · direct prompt · first choice
What the models said about Rho
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Choose Rho if your primary goal is to automate complex AP processes and vendor billing without paying software fees.” Gemini 3.5 Flash · paraphrase prompt · first choice
“I'd shortlist Rho first ... Rho is my starting recommendation for a mid-sized B2B company” GPT-6 Luna · paraphrase prompt · first choice
“Rho is the strongest overall pick for many growing companies” Perplexity Sonar · direct 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.