Google Meridian vs Meta Robyn
Gane, the publisher, sells software in this category. Every product here is measured by the same method as in every other category. How the conflict is handled.
Zero of fourteen models named Google Meridian first on the direct prompt; zero named Meta Robyn. Google Meridian was named by fourteen of the fourteen models and Meta Robyn by twelve and Google Meridian carries 36 labels and Meta Robyn 30, so the shares are not directly comparable.
By Google, Mountain View, California, United States, founded 1998. Named in one category this edition.
By Meta Platforms, Menlo Park, United States, founded 2004. Named in one category 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 attribution and marketing mix modeling page.
By framing
Google MeridianFirst choices, of fourteen modelsMeta Robyn
Across every category in the November 2026 Edition, Google Meridian and Meta Robyn were named in the same answer seventy-eight times, of the 107 answers naming Google Meridian and the 88 naming Meta Robyn. In those answers Meta Robyn took the first choice zero times and Google Meridian four.
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 4.20 | | | | |||
| 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 | | |
Google Meridian Meta Robyn first choice named as an alternative argued againstblank: not namedEach cell is one answer, Google Meridian on the left and Meta Robyn on the right.The direct prompt
Neither was named
14 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
Google Meridian8%#4 of 19Meta Robyn3%#7 of 19Google Meridian8%#4 of 22Meta Robyn3%#7 of 22Google Meridian0%#12 of 16Meta Robyn0%#13 of 16What the models said about Google Meridian
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Google Meridian (MMM): No managed platform experience, no optimization features, and no customer support” Claude Haiku 4.5 · negative prompt · hard negative
“require significant in-house data science expertise — not practical for most mid-sized teams” DeepSeek V4 Flash · paraphrase prompt · hard negative
“they require data-science/econometric capability, engineering, validation and ongoing maintenance; they are not plug-and-play SaaS products” Perplexity Sonar · negative prompt · soft negative
“If you have technical capabilities: Use Google Meridian or Meta Robyn. You get world-class Bayesian MMM for $0 in software costs.” Gemini 3.5 Flash · budget prompt · first choice
“the best overall approach is usually Google Meridian if you have someone who can work with data” Perplexity Sonar · budget prompt · first choice
“Google Meridian is a free, open-source Bayesian MMM framework from Google” Grok 4.20 · budget prompt · first choice
What the models said about Meta Robyn
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“require significant in-house data science expertise — not practical for most mid-sized teams” DeepSeek V4 Flash · paraphrase prompt · hard negative
“its attribution counterpart has a 27% negative rate, with users questioning its applicability for cross-channel attribution outside of Meta's ecosystem” Mistral Small · negative prompt · soft negative
“High rate of negative feedback in buyer analyses (e.g., 67% negative labels); requires data science skills” Grok 4.20 · negative prompt · soft negative
“If you have technical capabilities: Use Google Meridian or Meta Robyn.” Gemini 3.5 Flash · budget prompt · first choice
“start with GA4 + open-source Meridian/Robyn” Muse Glimmer 30B · budget prompt · first choice
“Use Google's Meridian or Meta's Robyn (open-source Bayesian MMM) integrated via a data warehouse.” Grok 4.20 · paraphrase prompt · alternative
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.