TwelveLabs Marengo 3.0
The most powerful embedding model for video understanding
Marengo 3.0 is TwelveLabs' most significant model to date, delivering human-like video understanding at scale. A multimodal embedding model, Marengo fuses video, audio, and text for holistic video understanding to power precise video search and retrieval.
Reviews
Great video understanding model! Would love to see scene segmentation features to automatically break long videos into topical chapters.
Fantastic for video analytics! The embeddings capture semantic meaning beyond just keywords. We built a content moderation system using Marengo - it identifies policy violations even when they're not explicitly stated. The scale handling is impressive - indexed 5TB of video content in 12 hours. Human-like video understanding is accurate! 🔥
Solid multimodal embedding model. The video understanding across visual, audio, and text modalities is accurate for video retrieval.
The embedding model works well but indexing very long videos (3+ hours) sometimes times out. Videos under 2 hours process perfectly.
Marengo 3.0 is a breakthrough for video search! We have 10,000+ hours of training video content and searching it was impossible before. Marengo's multimodal embeddings understand visual actions, spoken words, and on-screen text simultaneously. I searched "how to install the sensor module" and it found the exact 2-minute segment across 400 videos. The holistic understanding (video+audio+text) is what makes it work - previous tools only did text search. Indexed our entire library in 6 hours. Search accuracy is incredible - 95%+ relevant results. This is production-grade video understanding! 🚀
The API is well-designed with clear embedding endpoints. The search results include timestamp precision and relevance scores.
Impressive video understanding model. The semantic search across video, audio, and text is significantly better than keyword matching.
Perfect for media companies! We manage a news archive with 50,000+ video clips. Marengo makes searching by concept actually work - queries like "inflation discussion" or "protest footage" find relevant segments even when those exact words aren't spoken. The multimodal fusion understands visual context + audio + graphics. Journalists can now find B-roll footage in seconds instead of hours. The embedding quality is remarkable - semantically similar videos cluster together. This is the video search we've been waiting for! ✨
Love the multimodal approach! Feature request: similarity search to find videos similar to a given clip.
The search interface is fast with precise timestamp results. The relevance scoring helps prioritize results effectively.
Indie maker | Design enthusiast
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