diff --git a/qdrant-landing/content/documentation/embeddings/gemini.md b/qdrant-landing/content/documentation/embeddings/gemini.md
index 9a362680e..f672ec535 100644
--- a/qdrant-landing/content/documentation/embeddings/gemini.md
+++ b/qdrant-landing/content/documentation/embeddings/gemini.md
@@ -91,4 +91,21 @@ qdrant_client.search(
 )
 ```
 
-That's it! You can now use Gemini Embedding Models with Qdrant.
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+## Using Gemini Embedding Models with Binary Quantization
+
+You can use Gemini Embedding Models with [Binary Quantization](../../articles/binary-quantization.md) - a technique that allows you to reduce the size of the embeddings by 32 times without losing the quality of the search results too much. 
+
+In this table, you can see the results of the search with the `models/embedding-001` model with Binary Quantization in comparison with the original model:
+
+At an oversampling of 3 and a limit of 100, we've a 95% recall against the exact nearest neighbors with rescore enabled.
+
+| oversampling |         | 1        | 1        | 2        | 2        | 3        | 3        |
+|--------------|---------|----------|----------|----------|----------|----------|----------|
+| limit        |         |          |          |          |          |          |          |
+|              | rescore | False    | True     | False    | True     | False    | True     |
+| 10           |         | 0.523333 | 0.831111 | 0.523333 | 0.915556 | 0.523333 | 0.950000 |
+| 20           |         | 0.510000 | 0.836667 | 0.510000 | 0.912222 | 0.510000 | 0.937778 |
+| 50           |         | 0.489111 | 0.841556 | 0.489111 | 0.913333 | 0.488444 | 0.947111 |
+| 100          |         | 0.485778 | 0.846556 | 0.485556 | 0.929000 | 0.486000 | **0.956333** |
+
+That's it! You can now use Gemini Embedding Models with Qdrant!
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