# Measured vs Google Meridian: which do AI models recommend for Attribution & MMM, September 2026

GTM AI Recommendation Index, September 2026 Edition, Attribution and marketing mix modeling. Zero of twelve models named Measured first on the direct prompt; zero named Google Meridian. Page: https://gtm-ai-index.com/marketing/attribution-mmm/measured-vs-google-meridian/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Measured | 4% | #4 of 22 | 8% | 24 | 11 of 12 |
| Google Meridian | 3% | #7 of 22 | 25% | 36 | 11 of 12 |

## The direct prompt, model by model

- Mistral Small: neither first, one named (first choices: Dreamdata, Recast) (alternatives: Cometly, HockeyStack, Improvado, Measured, MixModeler, Ruler Analytics)
- GLM 4.7 FlashX: neither first, one named (first choices: Dreamdata, SegmentStream) (alternatives: Heeet, HockeyStack, Lifesight, Measured, MixModeler, Northbeam, Recast, Ruler Analytics)
- Claude Haiku 4.5: neither named (first choices: Dreamdata) (alternatives: CaliberMind, HubSpot Marketing Hub, Northbeam)
- GPT-5.4 mini: neither named (first choices: Dreamdata) (alternatives: Adobe Marketo Measure, CaliberMind, MixModeler)
- Gemini 3.5 Flash: neither named (first choices: CaliberMind) (alternatives: Dreamdata, HockeyStack, RevSure)
- Perplexity Sonar: neither named (first choices: Dreamdata) (alternatives: Factors.ai, HockeyStack)
- Grok 4.1 Fast: neither named (first choices: Dreamdata) (alternatives: HockeyStack, Recast, RevSure)
- DeepSeek V4 Flash: neither named (first choices: Dreamdata, HockeyStack, Rockerbox) (alternatives: Improvado, Recast, Ruler Analytics, SegmentStream, Sellforte)
- Llama 4 Maverick: neither named (first choices: Improvado) (alternatives: Dreamdata, Rockerbox)
- Qwen 3.7 Flash: neither named (first choices: Rockerbox) (alternatives: Alviss AI, Recast)
- Kimi K2: neither named (first choices: Dreamdata) (alternatives: Factors.ai, HockeyStack, Recast, Rockerbox)
- MiniMax M2.5: neither named (first choices: Dreamdata, Recast, SegmentStream) (alternatives: Analytic Edge, HubSpot Marketing Hub)

## What the models said about Measured

- "Avoid for B2B mid-market: Enterprise heavies (Adobe Mix Modeler, Measured: custom/$50K+)" (Grok 4.1 Fast, direct prompt, hard negative)
- "strong but more enterprise-oriented and stitched-together; pricing scales higher" (DeepSeek V4 Flash, paraphrase prompt, soft negative)
- "Tier 2 | Modern / SaaS-Enabled UMM & Causal MMM | Recast, Measured, Lifesight, Mutinex, LiftLab, Haus" (Gemini 3.5 Flash, scale prompt, first choice)
- "Measured | Enterprise hybrid; causal MMM + MTA ... Finance-friendly; geo-holdouts." (Grok 4.1 Fast, scale prompt, first choice)
- "Measured | Incrementality-first, finance-friendly, enterprise integrations" (Kimi K2, scale prompt, first choice)

## What the models said about Google Meridian

- "free/open-source (GA4, Google Meridian: lacks B2B depth)" (Grok 4.1 Fast, direct prompt, hard negative)
- "Meridian is statistical infrastructure, not a decision-making product. Requires a dedicated data science team to implement responsibly." (Kimi K2, negative prompt, soft negative)
- "Google has tied internal sales KPIs to Meridian adoption \u2014 account teams are incentivized to push it" (DeepSeek V4 Flash, negative prompt, soft negative)
- "Choose Meta Robyn or Google Meridian if you have basic R/Python skills and want high-end, enterprise-grade MMM completely free." (Gemini 3.5 Flash, budget prompt, first choice)
- "Use PyMC-Marketing or Google Meridian (both free)" (DeepSeek V4 Flash, budget prompt, first choice)
- "open-source (Google Meridian, Meta Robyn — both free)... Meridian is a genuine modern option if you have Python/PyMC expertise" (DeepSeek V4 Flash, scale prompt, alternative)

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve models, for a mid-market B2B company; rank is within the category. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.