Soda vs Monte Carlo
One of fourteen models named Soda first on the direct prompt; two named Monte Carlo. Both were named by all fourteen models and Soda carries 42 labels and Monte Carlo 53, so the shares are not directly comparable.
Named in one category this edition.
Named in three 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 data quality and observability page.
By framing
SodaFirst choices, of fourteen modelsMonte Carlo
Across every category in the October 2026 Edition, Soda and Monte Carlo were named in the same answer eighty-six times, of the 106 answers naming Soda and the 161 naming Monte Carlo. In those answers Monte Carlo took the first choice twenty-nine times and Soda fifteen.
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 | | | | | |
Soda Monte Carlo first choice named as an alternative argued againstblank: not namedEach cell is one answer, Soda on the left and Monte Carlo on the right.The direct prompt
Both were the first choice
1 of 14 modelsThe answer named them together, and the judge labeled each a first choice.Monte Carlo first, Soda not the choice
1 of 14 modelsSoda 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.Neither was named
4 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
Monte Carlo41%#1 of 12Soda4%#5 of 12What the models said about Soda
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Why be cautious: if you plan to stay on Soda Core open-source, assess ongoing support and feature parity; Soda Cloud pricing and roadmap are not self-serve” Muse Glimmer 30B · negative prompt · soft negative
“License change risk... Not full observability... Limited out-of-the-box metrics.” DeepSeek V4 Flash · negative prompt · soft negative
“Soda might require more upfront effort to configure tests” Claude Haiku 4.5 · negative prompt · soft negative
“Soda is often the better default because mid-sized teams usually benefit from faster adoption and built-in observability” GPT-5.4 mini · paraphrase prompt · first choice
“I'd shortlist Soda. It offers checks and monitoring across common warehouses and databases” GPT-6 Luna · paraphrase prompt · first choice
“best value choice is usually Great Expectations (GX Core) or Soda Free/open-source first” GPT-5.4 mini · budget prompt · first choice
What the models said about Monte Carlo
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Monte Carlo ... Noisy alerts ... Complex configuration ... Best avoided if: You need precise, actionable alerts or have limited resources” GLM 4.7 FlashX · negative prompt · hard negative
“Avoid jumping straight to Monte Carlo, Anomalo, or Informatica—those are overkill and overpriced” DeepSeek V4 Flash · paraphrase prompt · hard negative
“What to Avoid for Mid-Market - Monte Carlo: While excellent, it's built for large enterprises” Kimi K2 · direct prompt · hard negative
“Monte Carlo has built a strong reputation among mid-market and enterprise data teams and is well-suited for teams operating across complex, multi-tool stacks” Claude Haiku 4.5 · direct prompt · first choice
“The pioneer of the "data observability" category... Strongest end-to-end pipeline monitoring and incident workflow.” DeepSeek V4 Flash · comparative prompt · first choice
“The pioneer of the "data observability" category. It provides end-to-end coverage across your entire stack” Gemini 3.5 Flash · comparative 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.