diff --git a/qdrant-landing/content/documentation/embeddings/jina-embeddings.md b/qdrant-landing/content/documentation/embeddings/jina-embeddings.md
index 423d3595c..94e73d4c8 100644
--- a/qdrant-landing/content/documentation/embeddings/jina-embeddings.md
+++ b/qdrant-landing/content/documentation/embeddings/jina-embeddings.md
@@ -8,21 +8,57 @@ aliases:
 
 # Jina Embeddings
 
-Qdrant can also easily work with [Jina embeddings](https://jina.ai/embeddings/) which allow for model input lengths of up to 8192 tokens.
+Qdrant is compatible with [Jina AI](https://jina.ai/) embeddings. You can get a free trial key from [Jina Embeddings](https://jina.ai/embeddings/) to get embeddings.
+
+Qdrant users can receive a 10% discount on Jina AI APIs by using the code **QDRANT**.
+
+## Technical Summary
+
+|  Model | Dimension  |  Language |  MRL (matryoshka) | Context |
+|:----------------------:|:---------:|:---------:|:-----------:|:---------:|
+|  jina-embeddings-v3  |  1024 | Multilingual (89 languages)  |  Yes  | 8192 |
+|  jina-embeddings-v2-base-en |  768 |  English |  No | 8192  | 
+|  jina-embeddings-v2-base-de |  768 |  German & English |  No  |  8192 | 
+|  jina-embeddings-v2-base-es |  768 |  Spanish & English |  No  |  8192 | 
+|  jina-embeddings-v2-base-zh | 768  |  Chinese & English |  No  |  8192 | 
+
+> Jina recommends using `jina-embeddings-v3` as it is the latest and most performant embedding model released by Jina AI.
+
+On top of the backbone, `jina-embeddings-v3` has been trained with 5 task-specific adapters for different embedding uses. Include `task` in your request to optimize your downstream application:
+
++ **retrieval.query**: Used to encode user queries or questions in retrieval tasks.
++ **retrieval.passage**: Used to encode large documents in retrieval tasks at indexing time.
++ **classification**: Used to encode text for text classification tasks.
++ **text-matching**: Used to encode text for similarity matching, such as measuring similarity between two sentences.
++ **separation**: Used for clustering or reranking tasks.
+
+`jina-embeddings-v3` supports **Matryoshka Representation Learning**, allowing users to control the embedding dimension with minimal performance loss.  
+Include `dimensions` in your request to select the desired dimension.  
+By default, **dimensions** is set to 1024, and a number between 256 and 1024 is recommended.  
+You can reference the table below for hints on dimension vs. performance:
+
+
+|         Dimension          | 32 |  64  | 128 |  256   |  512   |   768 |  1024   | 
+|:----------------------:|:---------:|:---------:|:-----------:|:---------:|:----------:|:---------:|:---------:|
+|  Average Retrieval Performance (nDCG@10)   |   52.54     | 58.54 |    61.64    | 62.72 | 63.16  | 63.3  |   63.35    | 
+
+
+## Example
+
+The code below demonstrate how to use `jina-embeddings-v3` together with Qdrant:
 
-To call their endpoint, all you need is an API key obtainable [here](https://jina.ai/embeddings/). By the way, our friends from **Jina AI** provided us with a code (**QDRANT**) that will grant you a **10% discount** if you plan to use Jina Embeddings in production.
 
 ```python
-import qdrant_client
 import requests
 
+import qdrant_client
 from qdrant_client.models import Distance, VectorParams, Batch
 
 # Provide Jina API key and choose one of the available models.
-# You can get a free trial key here: https://jina.ai/embeddings/
 JINA_API_KEY = "jina_xxxxxxxxxxx"
-MODEL = "jina-embeddings-v2-base-en"  # or "jina-embeddings-v2-base-en"
-EMBEDDING_SIZE = 768  # 512 for small variant
+MODEL = "jina-embeddings-v3"
+DIMENSIONS = 1024 # Or choose your desired output vector dimensionality.
+TASK = 'retrieval.passage' # For indexing, or set to retrieval.query for quering
 
 # Get embeddings from the API
 url = "https://api.jina.ai/v1/embeddings"
@@ -35,6 +71,8 @@ headers = {
 data = {
     "input": ["Your text string goes here", "You can send multiple texts"],
     "model": MODEL,
+    "dimensions": DIMENSIONS,
+    "task": TASK,
 }
 
 response = requests.post(url, headers=headers, json=data)
@@ -45,7 +83,7 @@ embeddings = [d["embedding"] for d in response.json()["data"]]
 client = qdrant_client.QdrantClient(":memory:")
 client.create_collection(
     collection_name="MyCollection",
-    vectors_config=VectorParams(size=EMBEDDING_SIZE, distance=Distance.DOT),
+    vectors_config=VectorParams(size= DIMENSIONS, distance=Distance.DOT),
 )
 
 
@@ -58,4 +96,3 @@ qdrant_client.upsert(
 )
 
 ```
-