Moz Local vs Whitespark
Zero of fourteen models named Moz Local first on the direct prompt; zero named Whitespark. Moz Local was named by fourteen of the fourteen models and Whitespark by eight and Moz Local carries 50 labels and Whitespark 18, so the shares are not directly comparable.
Moz Local
accepted challengerNamed in three categories this edition.
Named in two 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 Whitespark were named in the same answer twenty-nine times, of the 141 answers naming Moz Local and the 40 naming Whitespark. In those answers Whitespark took the first choice three times and Moz Local three.
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 | |
WhitesparkML first choiceML named as an alternativeML argued againstblank: not namedEach cell is one answer, Moz Local on the left and Whitespark on the right.The direct prompt
Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
11 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
Whitespark4%#4 of 12Whitespark2%#8 of 10Whitespark0%#– 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 Whitespark
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“For the absolute lowest cost, Whitespark and BrightLocal are your best bets.” Mistral Small · budget prompt · first choice
“many reviewers cite switching to Whitespark or BrightLocal "for more affordable pricing and simpler contracts."” Muse Glimmer 30B · negative prompt · alternative
“Highly regarded for accuracy and Google integration. Often tops "most complete listings" lists; good value.” Grok 4.20 · 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.