YandexGPT · LLM monitoring
Russia’s #1 search + AI answers—YandexGPT
Users don’t always open a chat tab—Yandex weaves neural answers into Search. Yandex is Russia’s leading search engine, and YandexGPT is trained on Runet signals, so strong Russian-language presence translates into measurable mentions here, not in a US chatbot. Getllmspy runs organic prompts without naming your brand and surfaces who YandexGPT recommends.
Practical playbook
How we measure visibility (single expanded section). Every Getllmspy run locks a timestamp, model mix, and organic prompt pack—your brand is used for scoring, not pasted into questions, so mentions stay organic. We store YandexGPT answers alongside any other models you selected, then compute LLM-Score, share of voice, sentiment, competitors, and quotes. Re-running with the same configuration yields a comparable before/after slice. The structured cards below explain each report block without repeating this narrative three times.
What a finished report looks like
The sample stresses YandexGPT: its model row, mention rate, and quotes pulled only from YandexGPT answers.
Carapelli
Mentions by model (demo run)
Highlight: YandexGPT — 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 YandexGPT
Add sibling models in the same check to see if the pattern is specific to YandexGPT or repeats across the stack.
About this model
YandexGPT is not isolated from Search; it participates in smart-answer experiences where buyers shortlist vendors before clicking.
Runet-heavy brands get a measurable lift in Yandex-family answers versus models trained primarily on English corpora.
Why Russia & CIS matter here
For Russia-focused teams, YandexGPT is part of Search—not a siloed playground. Details on methodology and how to read metrics are in the section below and in the structured demo blocks.
How we measure visibility
Full methodology lives in the section below and in the report cards; in short: fixed packs, no brand name in prompts, compare YandexGPT with the other models you select.
- Compare YandexGPT with ChatGPT, Claude, Gemini, GigaChat, and more in one check
- Share of models mentioning you, tone, competitor names, and answer excerpts
- Timestamped reruns after positioning, content, or PR changes
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 YandexGPT 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 YandexGPT-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.
Few Western “AI visibility” platforms ship YandexGPT beside ChatGPT by default—Getllmspy does, so you aren’t optimizing US LLMs while Russian search narrates a different leaderboard.