MARENGO 3.5

media & entertainment

Find everything relevant. In every modality.

A multimodal embedding model that processes visual, audio, dialogue, and motion across 36 languages, returning a 512-dimensional vector ready for production search.

A multimodal embedding model that processes visual, audio, dialogue, and motion across 36 languages, returning a 512-dimensional vector ready for production search.

Unbounded

No video/audio duration cap.

36+

Languages for query and retrieval. Native, not translated.

512d

Embedding dimensions 6× smaller than other competitors.

30×

Faster than others at video indexing, while leading on accuracy.

Things only Marengo does.

Other foundation models stop at text-to-clip retrieval on short videos in English. Marengo starts there, then does the work that production systems actually need.

Other foundation models stop at text-to-clip retrieval on short videos in English. Marengo starts there, then does the work that production systems actually need.

Image

512 dimensions. Same understanding.

Marengo encodes video, audio, image, text, and documents into 512-dimensional embeddings, cutting storage 6x versus competitors and speeding searches without losing accuracy.

Image

An image plus a sentence, in one query.

Drop a player photo, add a sentence or two, and Marengo merges them into one embedding. Mix image, text, audio, and documents in a single query.

Image

36 languages, one space.

Search across 36 languages in Marengo's unified vector space. No translation step, no accuracy loss between languages.

Image

Search with fully composable queries.

Use an image query refined by text to find matching video moments.

From signup to first result in 5 minutes.

Same model, prompts, and JSON output. Choose the surface for your team.

Same model, prompts, and JSON output. Choose the surface for your team.

Python
Node.js
1import requests
2 
3# Step 2: Define the API URL and the specific endpoint
4API_URL = "https://api.twelvelabs.io/v1.3"
5INDEXES_URL = f"{API_URL}/indexes"
6 
7# Step 3: Create the necessary headers for authentication
8headers = {
9 "x-api-key": "<YOUR_API_KEY>"
10}
11 
12# Step 4: Prepare the data payload for your API request
13INDEX_NAME = "<YOUR_INDEX_NAME>"
14data = {
15 "models": [
16 {
17 "model_name": "marengo3.0",
18 "model_options": ["visual", "audio"]
19 }
20 ]
21}

Faster, smaller, more accurate.

Marengo was designed against production workloads, not benchmarks, demos, or three-minute clips. Here's what that means in practice.

Marengo was designed against production workloads, not benchmarks, demos, or three-minute clips. Here's what that means in practice.

Composite video-retrieval performance

CAPABILITY

MARENGO 3.5

Gemini Embedding 2

Nova Multimodal embeddings

Embedding dimensions

512

3072 (default; 1536 / 768 via Matryoshka)

3072 (default; 1024 / 384 / 256 via Matryoshka)

Max video length

Unbounded (no duration cap)

120 sec per request

30 sec per segment (chunked)

Multimodal Composite Queries

Yes - video, audio, image, text, and documents composed in one query (16K-token sync endpoint))

No native composite query API

No native composite query API

Sports recognition

Leads by 12–38 pts vs. GE2 on sports/image-query/image+text search

Baseline

Trails Marengo 3.5 by 35–45 pts

Embedding uncertainty signal

Yes - optional uncertainty signal on every embedding

None

None

Start building with TwelveLabs

Bring video intelligence into your production and archive workflows. Deploy, scale, and get more from your content.

Start building with TwelveLabs

Bring video intelligence into your production and archive workflows. Deploy, scale, and get more from your content.

Start building with TwelveLabs

Bring video intelligence into your production and archive workflows. Deploy, scale, and get more from your content.