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Average Ratings 0 Ratings
Description
Marengo is an advanced multimodal model designed to convert video, audio, images, and text into cohesive embeddings, facilitating versatile “any-to-any” capabilities for searching, retrieving, classifying, and analyzing extensive video and multimedia collections. By harmonizing visual frames that capture both spatial and temporal elements with audio components—such as speech, background sounds, and music—and incorporating textual elements like subtitles and metadata, Marengo crafts a comprehensive, multidimensional depiction of each media asset. With its sophisticated embedding framework, Marengo is equipped to handle a variety of demanding tasks, including diverse types of searches (such as text-to-video and video-to-audio), semantic content exploration, anomaly detection, hybrid searching, clustering, and recommendations based on similarity. Recent iterations have enhanced the model with multi-vector embeddings that distinguish between appearance, motion, and audio/text characteristics, leading to marked improvements in both accuracy and contextual understanding, particularly for intricate or lengthy content. This evolution not only enriches the user experience but also broadens the potential applications of the model in various multimedia industries.
Description
Qwen3-Omni is a comprehensive multilingual omni-modal foundation model designed to handle text, images, audio, and video, providing real-time streaming responses in both textual and natural spoken formats. Utilizing a unique Thinker-Talker architecture along with a Mixture-of-Experts (MoE) framework, it employs early text-centric pretraining and mixed multimodal training, ensuring high-quality performance across all formats without compromising on text or image fidelity. This model is capable of supporting 119 different text languages, 19 languages for speech input, and 10 languages for speech output. Demonstrating exceptional capabilities, it achieves state-of-the-art performance across 36 benchmarks related to audio and audio-visual tasks, securing open-source SOTA on 32 benchmarks and overall SOTA on 22, thereby rivaling or equaling prominent closed-source models like Gemini-2.5 Pro and GPT-4o. To enhance efficiency and reduce latency in audio and video streaming, the Talker component leverages a multi-codebook strategy to predict discrete speech codecs, effectively replacing more cumbersome diffusion methods. Additionally, this innovative model stands out for its versatility and adaptability across a wide array of applications.
API Access
Has API
API Access
Has API
Integrations
ConvNetJS
GPT-4o
Gemini 2.5 Pro
Gemini 2.5 Pro Deep Think
Gemini 3 Deep Think
Hermes Agent
OpenClaw
TwelveLabs
Integrations
ConvNetJS
GPT-4o
Gemini 2.5 Pro
Gemini 2.5 Pro Deep Think
Gemini 3 Deep Think
Hermes Agent
OpenClaw
TwelveLabs
Pricing Details
$0.042 per minute
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
TwelveLabs
Founded
2021
Country
United States
Website
www.twelvelabs.io/product/models-overview#marengo
Vendor Details
Company Name
Alibaba
Founded
1999
Country
China
Website
qwen.ai/blog