Three of fourteen models named Rockerbox first on the direct prompt; zero named Google Meridian. Rockerbox was named by ten of the fourteen models and Google Meridian by thirteen and Rockerbox carries 29 labels and Google Meridian 34, so the shares are not directly comparable.
Named in one category this edition.
By Google, Mountain View, California, United States, founded 1998. 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.
Across every category in the October 2026 Edition, Rockerbox and Google Meridian were named in the same answer twenty-two times, of the 75 answers naming Rockerbox and the 89 naming Google Meridian. In those answers Google Meridian took the first choice three times and Rockerbox four.
| 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 |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Be very cautious with Rockerbox unless you have a full-time developer and accept biased reporting and data reconciliation issues.” DeepSeek V4 Flash · negative prompt · soft negative
“Tools like LeadsRx or Rockerbox require heavy data prep; poor support or limited APIs lead to frustration.” Grok 4.1 Fast · negative prompt · soft negative
“platforms (like Recast, Measured, or Rockerbox) routinely cost upwards of $50,000 to $150,000+ per year” Gemini 3.5 Flash · budget prompt · soft negative
“The best marketing attribution and marketing mix modeling platform for a mid-market B2B company is Rockerbox” Llama 4 Maverick · direct prompt · first choice
“paired with Rockerbox or Google's Meridian (open-source MMM) for marketing mix modeling (MMM)” Grok 4.1 Fast · paraphrase prompt · first choice
“most mid-market B2B teams start with Rockerbox for the MTA+MMM combo to avoid two vendors” Muse Glimmer 30B · paraphrase prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Free, open-source libraries like Google Meridian or Meta Robyn require dedicated data scientists... a SaaS platform with a managed service component is almost always the right choice” Gemini 3.5 Flash · scale prompt · soft negative
“Mid-market companies rarely have full-time data scientists to manage open-source MMM code (like Google Meridian or Meta Robyn)” Gemini 3.5 Flash · direct prompt · soft negative
“Google Meridian if you lack data or modeling support. Meridian is open source, not a turnkey measurement service.” GPT-6 Luna · negative prompt · soft negative
“open-source MMM tools like Meta's Robyn or Google's Meridian are increasingly attractive due to their transparency and cost-effectiveness” Mistral Small · negative prompt · first choice
“Open-source Bayesian MMM, native Google Ads integration... Successor to LightweightMMM; free, transparent code” GLM 4.7 FlashX · comparative prompt · first choice
“Google's Meridian is a Bayesian MMM framework released openly for anyone to use—no licensing fees.” Grok 4.1 Fast · 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.