# Recast vs Cometly: which do AI models recommend for Attribution & MMM, October 2026

GTM AI Recommendation Index, October 2026 Edition, Attribution and marketing mix modeling. Zero of fourteen models named Recast first on the direct prompt; zero named Cometly. Page: https://gtm-ai-index.com/marketing/attribution-mmm/recast-vs-cometly/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Recast | 3% | #7 of 17 | 13% | 15 | 9 of 14 |
| Cometly | 2% | #8 of 17 | 7% | 15 | 8 of 14 |

## The direct prompt, model by model

- Kimi K2: neither first, one named (first choices: Dreamdata) (alternatives: Factors.ai, Recast, Rockerbox)
- GLM 4.7 FlashX: neither first, one named (first choices: Dreamdata) (alternatives: Cometly, HubSpot Marketing Hub, Keen Decision Systems, Rockerbox, Ruler Analytics)
- Claude Haiku 4.5: neither named (first choices: Dreamdata, HockeyStack) (alternatives: CaliberMind, HubSpot Marketing Hub, Rockerbox, SegmentStream)
- GPT-5.4 mini: neither named (first choices: Dreamdata) (alternatives: HockeyStack, Measured)
- Gemini 3.5 Flash: neither named (first choices: CaliberMind) (alternatives: Dreamdata, HockeyStack, RevSure, SegmentStream)
- Perplexity Sonar: neither named (first choices: Dreamdata) (alternatives: HockeyStack, Improvado, SegmentStream)
- Grok 4.1 Fast: neither named (first choices: Dreamdata) (alternatives: Factors.ai, HockeyStack, Rockerbox)
- Mistral Small: neither named (first choices: Dreamdata, MixModeler) (alternatives: Northbeam)
- DeepSeek V4 Flash: neither named (first choices: Dreamdata, Rockerbox) (alternatives: HockeyStack, Marketing Evolution, Measured)
- Llama 4 Maverick: neither named (first choices: Rockerbox) (alternatives: Attribution, Dreamdata, Northbeam)
- Qwen 3.7 Flash: neither named (first choices: Rockerbox) (alternatives: Dreamdata)
- MiniMax M2.5: neither named (first choices: HockeyStack, Marketing Evolution) (alternatives: Adobe Marketo Measure, Dreamdata)
- GPT-6 Luna: neither named (first choices: HockeyStack) (alternatives: Dreamdata, Paramark)
- Muse Glimmer 30B: neither named (first choices: Dreamdata) (alternatives: Admetrics, Adobe Marketo Measure, HockeyStack, Rockerbox)

## What the models said about Recast

- "Enterprise-grade, managed MMM and multi-touch attribution (MTA) 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)
- "Recast has limitations if you need custom transformations—like modeling promo mechanics with different lag structures per SKU." (Claude Haiku 4.5, negative prompt, soft negative)
- "Recast | Self-serve Bayesian MMM, fast refresh, no data science team needed | Companies wanting modern MMM without heavy lift" (Kimi K2, scale prompt, first choice)
- "If you have 1-2 analysts with intermediate statistics experience, look closely at Recast or Haus." (Gemini 3.5 Flash, scale prompt, first choice)
- "Add MMM with: Recast ... offers the best balance of modern methodology and mid-market accessibility" (Kimi K2, paraphrase prompt, alternative)

## What the models said about Cometly

- "Cometly is built for digital-only marketers; if more than 20% of your spend is in offline channels, it doesn't support those natively" (Claude Haiku 4.5, negative prompt, soft negative)
- "Cometly (Best for Cost-Effective Multi-Channel Optimization) ... Choose this if you want actionable, daily insights to optimize ad spend without a massive price tag." (Qwen 3.7 Flash, budget prompt, first choice)
- "Cometly - a self-service attribution platform that combines real-time multi-touch attribution with MMM-style aggregated modeling." (Llama 4 Maverick, comparative prompt, first choice)
- "Choose Cometly if you’re a performance‑marketing team with high ad spend and need server‑side accuracy." (GLM 4.7 FlashX, direct prompt, alternative)

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. 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.