Moz Local vs SOCi
Zero of fourteen models named Moz Local first on the direct prompt; two named SOCi. Moz Local was named by fourteen of the fourteen models and SOCi by twelve and Moz Local carries 50 labels and SOCi 22, so the shares are not directly comparable.
Moz Local
accepted challengerNamed in three categories this edition.
Named in four 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 local listings management page.
By framing
Across every category in the November 2026 Edition, Moz Local and SOCi were named in the same answer thirty-six times, of the 141 answers naming Moz Local and the 134 naming SOCi. In those answers SOCi took the first choice two times and Moz Local five.
Every model, every framing
| Model | DirectML | ParaphraseML | ComparativeML | Budget-constrainedML | Scale-constrainedML | NegativeML |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ML | ML | ML | |||
| GPT-5.4 mini | ML | ML | ML | |||
| Gemini 3.5 Flash | ML | ML | ML | |||
| Perplexity Sonar | | ML | ML | ML | ||
| Grok 4.20 | ML | ML | ML | ML | ML | |
| Mistral Small | ML | ML | ML | |||
| DeepSeek V4 Flash | ML | ML | ML | ML | ||
| Llama 4 Maverick | ML | ML | ||||
| Qwen 3.7 Flash | ML | ML | ||||
| Kimi K2 | ML | ML | ML | ML | | ML |
| GLM 4.7 FlashX | ML | ML | ML | ML | ||
| MiniMax M2.5 | | ML | ML | ML | ||
| GPT-6 Luna | | ML | ||||
| Muse Glimmer 30B | ML | ML | ML |
SOCiML first choiceML named as an alternativeML argued againstblank: not namedEach cell is one answer, Moz Local on the left and SOCi on the right.The direct prompt
SOCi first, Moz Local not the choice
2 of 14 modelsMoz Local was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
4 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
8 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
SOCi0%#10 of 12SOCi4%#5 of 10SOCi20%#2 of 10What the models said about Moz Local
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Why to avoid: - Email overload — Users report excessive, unmanageable notifications” GLM 4.7 FlashX · negative prompt · hard negative
“Who should avoid: Agencies, enterprises, or anyone needing fast syncing/automation.” Grok 4.20 · negative prompt · hard negative
“Other platforms like BrightLocal and Moz Local have also received some negative feedback, but they also offer useful features” Llama 4 Maverick · negative prompt · soft negative
“The best local listings management platform for a company with a limited budget is Moz Local, as it is the most affordable option” Llama 4 Maverick · budget prompt · first choice
“Moz Local is best for affordable automated listing distribution, offering the best entry price with solid core features.” Claude Haiku 4.5 · budget prompt · first choice
“For a single-location company on a tight budget, Moz Local at $16/month is the sweet spot” DeepSeek V4 Flash · budget prompt · first choice
What the models said about SOCi
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Generally enterprise-oriented with custom pricing and complex workflows. Consider them cautiously” Perplexity Sonar · negative prompt · soft negative
“SOCi (often stylized as SOCi) stands out as the best local listings management platform for a mid-market B2B company” Grok 4.20 · direct prompt · first choice
“I'd recommend starting with SOCi or SEMrush” MiniMax M2.5 · direct 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.