Mistral · LLM monitoring
EU stack, dev teams, internal bots—Mistral AI
EU revenue and GDPR posture matter, yet many partners ship internal copilots off Mistral—not OpenAI. Mistral is a French vendor with open weights and enterprise SKUs; engineers embed it everywhere. A Mistral slice shows whether European-facing AI knows your brand where procurement actually happens.
What a finished report looks like
The sample stresses Mistral’s row and excerpts from Mistral answers beside US chat models in the same grid.
Carapelli
Mentions by model (demo run)
Highlight: Mistral — 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 Mistral
Add sibling models in the same check to see if the pattern is specific to Mistral or repeats across the stack.
About this model
Mistral markets itself as the European counterweight to US hyperscalers—important when data residency and vendor geography are part of RFPs.
Open-weight releases power custom corporate assistants: you can be visible in public ChatGPT yet absent inside a customer’s Mistral-hosted bot—two deployments, one family.
How we measure visibility
Organic prompt packs; no brand name in questions; consistent Getllmspy metrics.
- Mistral next to ChatGPT, Claude, Gemini, and more
- LLM-Score, share of voice, competitors
- Quotes for wording review
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 Mistral 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 Mistral-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.
Large Mistral vs ChatGPT gaps may reflect corpus differences—factor into localization.