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Treasury management systems · November 2026 Edition

Kyriba vs Mercury

Three of fourteen models named Kyriba first on the direct prompt; zero named Mercury. Kyriba was named by fourteen of the fourteen models and Mercury by eight and Kyriba carries 49 labels and Mercury 11, so the shares are not directly comparable.

Kyriba

criticized challenger

Named in five categories this edition.

Mercury

accepted challenger

Named in four categories this edition.

First-choice share8%8%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate35%18%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#3A position in a field of 11; printed, not drawn.
Labels4911A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Kyriba reading right to left. Rank and label count are printed, not drawn.Trovata was named alongside these two in thirteen of the fourteen direct answers. Trovata vs Kyriba · Trovata vs Mercury · Kyriba vs Agicap

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 treasury management systems page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
KyribaFirst choices, of fourteen modelsMercury
Direct303 against Kyriba
Paraphrase00
Comparative100
Budget-constrained048 against Kyriba
Scale-constrained10
Negative006 against Kyriba · 2 against Mercury
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Kyriba and Mercury stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.20
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
Kyriba Mercury first choice named as an alternative argued againstblank: not namedEach cell is one answer, Kyriba on the left and Mercury on the right.

The direct prompt

The plain question, one answer per model, grouped by where Kyriba and Mercury stood in it.

Kyriba first, Mercury not the choice

3 of 14 modelsMercury was named in the answer but not as the choice, or not at all.
GPT-5.4 miniKyriba alternatives: Coupa Treasury, Nomentia, Trovata
Qwen 3.7 FlashKyriba alternatives: GTreasury, Trovata
GPT-6 LunaKyriba alternatives: Ripple Treasury, Trovata

Neither was the first choice, one was named

4 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashTrovata alternatives: Centime, Embat, HighRadius Treasury Management, Kyriba, Nomentia
DeepSeek V4 FlashTrovata alternatives: Embat, GTreasury, Kyriba
Kimi K2Trovata alternatives: Agicap, Embat, Kyriba, Nilus, Panax
GLM 4.7 FlashXTrovata alternatives: Centime, Kyriba, Panax

Neither was named

7 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5no first choice
Perplexity SonarTrovata alternatives: Agicap, Embat, GTreasury, HighRadius Treasury Management, Nilus
Grok 4.20Trovata alternatives: Agicap, GTreasury
Mistral SmallTrovata alternatives: ATOM Treasury & Risk Management, Embat, HighRadius Treasury Management
Llama 4 MaverickTrovata alternatives: Agicap, Embat, GTreasury
MiniMax M2.5GTreasury alternatives: Centime, TreasuryView, Trovata
Muse Glimmer 30BTrovata alternatives: Agicap, Embat, FIS Quantum Express, GTreasury, Panax, Treasura, TreasuryXpress

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Mercury leads by ten points.
Mercury10%#2 of 14
Kyriba0%#14 of 14
The full small business standing →
Mid-marketThe figures above
Level: the same share of first choices.
Kyriba8%#2 of 11
Mercury8%#3 of 11
The full mid-market standing →
Enterprise
The order flips: Kyriba leads at enterprise.
Kyriba43%#1 of 12
Mercury0%#– of 12
The full enterprise standing →

What the models said about Kyriba

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.

“avoid enterprise tools like Kyriba or GTreasury that charge premiums for those unused features” Qwen 3.7 Flash · budget prompt · hard negative
“Avoid enterprise platforms such as Kyriba, SAP Treasury, or GTreasury” Perplexity Sonar · budget prompt · hard negative
“Broad cloud treasury suite: cash and liquidity, forecasting, payments, bank connectivity, working capital, and risk. A common shortlist candidate for multinationals” GPT-6 Luna · comparative prompt · first choice
“Kyriba is widely considered the leader in bank connectivity... It strikes the best balance between powerful risk management and usability/payments features.” Qwen 3.7 Flash · comparative prompt · first choice
“Choose Kyriba for a broadly capable, independent enterprise TMS with global connectivity.” Perplexity Sonar · comparative prompt · first choice

What the models said about Mercury

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.

“starting with a modern business banking platform like Mercury or Ramp might be the most cost-effective approach” MiniMax M2.5 · budget prompt · first choice
“Mercury — *Best for Startups and Small Businesses* ... $0/month for banking and basic treasury features” Kimi K2 · budget prompt · first choice
Also compared

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.