RadarScore

Mistral

↑ 0.6

llmfreemium✓ Self-hostable

Website ↗GitHub ↗

Sub-Score Breakdown

buzz100.0

recency0.0

adoption Momentum43.2

developer Sentiment57.5

enterprise Readiness21.6

Score History

Latest Signals

github10/10/2026

{
  "forks": 1062,
  "stars": 10822,
  "openIssues": 1,
  "starsGrowth30d": 216,
  "lastReleaseDate": "2025-03-20T15:03:08Z",
  "commitFrequency90d": 0
}

changelog10/10/2026

{
  "latestVersion": "v1.6.0",
  "releasesLast90d": 0,
  "daysSinceLastRelease": 568
}

arxiv10/10/2026

{
  "paperCount": 50,
  "recentPapers30d": 50
}

hn10/10/2026

{
  "avgSentiment": 0.149,
  "totalPoints30d": 4676,
  "commentCount30d": 2651,
  "frontPageHits30d": 12
}

github10/10/2026

{
  "forks": 1062,
  "stars": 10822,
  "openIssues": 1,
  "starsGrowth30d": 216,
  "lastReleaseDate": "2025-03-20T15:03:08Z",
  "commitFrequency90d": 0
}

changelog10/10/2026

{
  "latestVersion": "v1.6.0",
  "releasesLast90d": 0,
  "daysSinceLastRelease": 568
}

Comparisons

Mistral vs LLaMA

LLaMA and Mistral are both prominent open-source large language models that compete in the same space, with LLaMA being Meta's flagship model family and Mistral representing a European AI company's approach. LLaMA generally offers larger model variants and has broader research adoption, while Mistral focuses on efficiency and commercial-friendly licensing. Both provide strong performance for their respective niches, with LLaMA having slight advantages in raw capability and Mistral excelling in deployment flexibility.