Semrush vs Frase
Gane, the publisher, sells software in this category. Every product here is measured by the same method as in every other category. How the conflict is handled.
Eight of fourteen models named Semrush first on the direct prompt; zero named Frase. Both were named by all fourteen models and Semrush carries 57 labels and Frase 42, so the shares are not directly comparable.
Boston, United States, founded 2008. Named in six categories this edition.
Named in three 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 SEO and content optimization platforms page.
By framing
SemrushFirst choices, of fourteen modelsFrase
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 4.20 | | | | | | |
| 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 | | | | | |
Semrush Frase first choice named as an alternative argued againstblank: not namedEach cell is one answer, Semrush on the left and Frase on the right.The direct prompt
Semrush first, Frase an alternative
8 of 14 modelsFrase 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
2 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
What the models said about Semrush
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“Don't pay for expensive tools like Semrush ($139+/month)” MiniMax M2.5 · budget prompt · hard negative
“the best overall SEO and content optimization platform is a combination of Semrush (or Ahrefs) for research/strategy + Surfer SEO (or Clearscope) for content optimization” Grok 4.20 · direct prompt · first choice
“Best for: Full digital marketing suites, agencies, power users ... Largest keyword database (26.6B Google keywords)” Kimi K2 · comparative prompt · first choice
“For most mid-market B2B companies, Semrush is the best overall SEO and content optimization platform” Perplexity Sonar · direct prompt · first choice
What the models said about Frase
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoid premium-heavy tools like Clearscope, SurferSEO, or Frase” Grok 4.20 · budget prompt · hard negative
“for example, Surfer, Clearscope, or Frase. They can help with research and coverage, but don’t treat a score, suggested word count, or list of terms as a ranking formula.” GPT-6 Luna · negative prompt · soft negative
“Frase has no backlink analysis, technical SEO auditing, or AI writing, so you'd need additional tools for comprehensive SEO work.” Claude Haiku 4.5 · negative prompt · soft negative
“Best Overall for Budget: Frase is the most affordable tool that combines research, brief generation, AI writing, and optimization” Claude Haiku 4.5 · budget prompt · first choice
“Content Workflow: Frase or Surfer (research → brief → write → optimize)” GLM 4.7 FlashX · comparative prompt · first choice
“NeuronWriter and Frase are the best choices for a limited budget” Mistral Small · budget 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.