Eight of fourteen models named BrightLocal first on the direct prompt; three named Yext. Both were named by all fourteen models and BrightLocal carries 67 labels and Yext 66, so the shares are not directly comparable.
Named in four categories this edition.
New York City, United States, founded 2006. Named in five 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.
Across every category in the November 2026 Edition, BrightLocal and Yext were named in the same answer 157 times, of the 222 answers naming BrightLocal and the 295 naming Yext. In those answers Yext took the first choice twenty-five times and BrightLocal fifty-eight.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | ||||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | ||||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Who should avoid: Larger operators or agencies managing dozens of listings.” Grok 4.20 · negative prompt · hard negative
“BrightLocal is often recommended, but buyers note persistent issues: "eight persistent issues, including performance bottlenecks, a dated user experience, unpredictable costs"” Muse Glimmer 30B · negative prompt · soft 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
“BrightLocal is the top recommendation due to its comprehensive features, strong reputation, and value for multi-location management.” Mistral Small · paraphrase prompt · first choice
“BrightLocal is the best fit for agencies and local brands needing recurring multi-location listing audits with audit-ready reporting.” Muse Glimmer 30B · comparative prompt · first choice
“BrightLocal: Strong for agencies/SMBs scaling up. Excellent listings sync, reporting, and review tools. Affordable entry point” Grok 4.20 · scale prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Yext: widely regarded as powerful but notoriously expensive and complex for smaller entities. Users frequently complain about high price hikes and poor support tiers” Qwen 3.7 Flash · negative prompt · hard negative
“Yext is one such platform that has received negative reviews and is not recommended for mid-sized B2B companies” Llama 4 Maverick · negative prompt · hard negative
“Yext – rental model / post-cancellation drift ... "You are renting your citations, not building an asset."” Muse Glimmer 30B · negative prompt · hard negative
“Yext and Uberall both position their products for multi-location brands, with bulk updates, publisher networks, duplicate suppression, and enterprise workflows.” GPT-5.4 mini · scale prompt · first choice
“Yext | Enterprise brands, multi-location franchises | ~$16/month (per platform) | Centralized management, massive publisher network (200+)” GLM 4.7 FlashX · comparative prompt · first choice
“I'd usually recommend Yext if your top priority is centralized listings control, governance, and consistency across many locations” GPT-5.4 mini · paraphrase 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.