Meta Robyn vs HockeyStack
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 Meta Robyn first on the direct prompt; one named HockeyStack. Meta Robyn was named by twelve of the fourteen models and HockeyStack by eleven and both carry 30 labels, so the shares below are directly comparable.
By Meta Platforms, Menlo Park, United States, founded 2004. Named in one category 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 attribution and marketing mix modeling page.
By framing
Meta RobynFirst choices, of fourteen modelsHockeyStack
Across every category in the November 2026 Edition, Meta Robyn and HockeyStack were named in the same answer thirty-one times, of the 88 answers naming Meta Robyn and the 101 naming HockeyStack. In those answers HockeyStack took the first choice one time and Meta Robyn zero.
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 | | |
Meta Robyn HockeyStack first choice named as an alternative argued againstblank: not namedEach cell is one answer, Meta Robyn on the left and HockeyStack on the right.The direct prompt
HockeyStack first, Meta Robyn not the choice
1 of 14 modelsMeta Robyn was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
6 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
7 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
Meta Robyn3%#7 of 19HockeyStack2%#10 of 19Meta Robyn3%#7 of 22HockeyStack2%#8 of 22HockeyStack7%#5 of 16Meta Robyn0%#13 of 16What 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
What the models said about HockeyStack
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“Best Overall for Mid-Market B2B” Kimi K2 · direct prompt · first choice
“Prioritize account-level attribution with Salesforce/HubSpot native integration (Dreamdata, HockeyStack, or Adobe Marketo Measure)” Kimi K2 · scale 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.