DeepSeek · LLM monitoring
Asia-scale usage, different training stack—DeepSeek
You benchmark ChatGPT, while another cohort already uses a Chinese assistant with free access and a huge Asian user base. DeepSeek is trained on a different corpus than Western flagships—brands often see surprising over- or under-indexing. A DeepSeek slice answers how you look there, not only in US-centric chats.
What a finished report looks like
The demo highlights DeepSeek’s row: how often it mentions the brand and what it actually says in your scenarios.
Carapelli
Mentions by model (demo run)
Highlight: DeepSeek — the focus of this landing page. Numbers are illustrative.
Competitors in this slice
Your real report uses the same layout: scores, per-model breakdown, quotes, competitors, and citations — with your brand and the models you select.
Benchmarking
Timestamped snapshot
Completion time is stored with every run—clean before/after comparisons when you change positioning or content.
Method
Organic-style prompts
Your brand name is not pasted into the question text; we score whether models still mention you in realistic category queries.
Context
Around DeepSeek
Add sibling models in the same check to see if the pattern is specific to DeepSeek or repeats across the stack.
About this model
DeepSeek’s distribution skews heavily toward Asia; if expansion east matters, this is the first practical signal of whether you appear in that AI layer.
Because data and alignment differ from OpenAI/Anthropic, competitor lists can reshuffle entirely. Free tiers accelerate adoption globally, including Russia.
How we measure visibility
Standard organic prompt packs; no brand name in questions; metrics align with other Getllmspy models.
- DeepSeek + ChatGPT + Claude + Gemini and more in one run
- LLM-Score, share of voice, competitors
- Quotes for manual fact checking
Inside the report
Snapshot header
Completion time and which models ran—your anchor for before/after benchmarking.
LLM-Score & share of voice
Aggregated 0–100 signal plus the share of models that mentioned your brand at least once.
Competitors & roundups
Who appears next to you in DeepSeek answers: names, frequency, comparison or recommendation context.
Quotes & wording
Answer excerpts for manual review—how the model talks about the category and your brand.
Same prompts on other models
Parallel runs (Claude, Gemini, Perplexity, …) to see if the pattern is DeepSeek-specific.
From check to PDF-ready snapshot
Brand & niche
You set brand context, site, category, language, and check type—this selects the prompt pack.
Model mix
Pick the LLM families to include; the same scenarios run in parallel across all of them.
Server run
The job executes on our side; you can close the tab and open the report from History when ready.
Report
LLM-Score, share of voice, competitors, quotes, citations—exportable and rerunnable on demand.
If DeepSeek lists rivals but not you, strengthen docs, integrations, and technical PR.