Recast vs Measured
Two of twelve models named Recast first on the direct prompt; zero named Measured. Recast was named by ten of the twelve models and Measured by eleven and Recast carries 35 labels and Measured 24, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
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; every quote names the model and the prompt it came from. Both figures come from the attribution and marketing mix modeling page.
By framing
RecastFirst choices, of twelve modelsMeasured
Across every category in the September 2026 Edition, Recast and Measured were named in the same answer forty-nine times, of the 79 answers naming Recast and the 70 naming Measured. In those answers Measured took the first choice seven times and Recast 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 4.1 Fast | | | | | | |
| Mistral Small | | | | |||
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | |||||
| Qwen 3.7 Flash | | |||||
| Kimi K2 | | | | | ||
| GLM 4.7 FlashX | | | ||||
| MiniMax M2.5 | |
Recast Measured first choice named as an alternative argued againstblank: not namedEach cell is one answer, Recast on the left and Measured on the right.The direct prompt
Recast first, Measured an alternative
2 of 12 modelsMeasured was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
5 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
5 of 12 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
Recast0%#14 of 18Measured0%#17 of 18Measured9%#4 of 21Recast2%#10 of 21What the models said about Recast
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Far too expensive for limited budgets” DeepSeek V4 Flash · budget prompt · hard negative
“Built for Statisticians, Not Marketers ... Requires a data scientist to translate Bayesian posteriors” Kimi K2 · negative prompt · soft negative
“Recast/Funnel are solid but more digital-heavy/enterprise (~$50K+)” Grok 4.1 Fast · paraphrase prompt · soft negative
“You likely want to focus on Tier 2 (SaaS-Enabled UMM): Recast, Measured, Lifesight, Mutinex, LiftLab, Haus” Gemini 3.5 Flash · scale prompt · first choice
“Recast and Measured being more suitable for mid-market digital teams and enterprises, respectively” Llama 4 Maverick · scale prompt · first choice
“Recast is the standout — transparent Bayesian models, weekly refreshes, and a fast setup” DeepSeek V4 Flash · paraphrase prompt · first choice
What the models said about Measured
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“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
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.