diff --git a/docs/weaviate/model-providers/google/embeddings-multimodal.md b/docs/weaviate/model-providers/google/embeddings-multimodal.md
index da3c7925..ef2c8073 100644
--- a/docs/weaviate/model-providers/google/embeddings-multimodal.md
+++ b/docs/weaviate/model-providers/google/embeddings-multimodal.md
@@ -19,7 +19,7 @@ import TSCode from '!!raw-loader!../_includes/provider.vectorizer.ts';
 Weaviate's integration with [Google Gemini API](https://ai.google.dev/?utm_source=weaviate&utm_medium=referral&utm_campaign=partnerships&utm_content=) and [Google Vertex AI](https://cloud.google.com/vertex-ai) APIs allows you to access their models' capabilities directly from Weaviate.
 
 :::note Gemini API multimodal support
-The `gemini-embedding-2-preview` model supports multimodal embeddings (text, images, and PDFs) and is available via both Vertex AI and Google AI Studio (Gemini API). The `multimodalembedding@001` model remains available for Vertex AI users only.
+The `gemini-embedding-2` model supports multimodal embeddings (text, images, and PDFs) and is available via both Vertex AI and Google AI Studio (Gemini API). The `multimodalembedding@001` model remains available for Vertex AI users only.
 :::
 
 [Configure a Weaviate vector index](#configure-the-vectorizer) to use a Google embedding model, and Weaviate will generate embeddings for various operations using the specified model and your Google API key. This feature is called the *vectorizer*.
@@ -164,8 +164,8 @@ The following examples show how to configure Google-specific options.
 - `location` (Required): e.g. `"us-central1"`
 - `projectId` (Only required if using Vertex AI): e.g. `cloud-large-language-models`
 - `apiEndpoint` (Optional): e.g. `us-central1-aiplatform.googleapis.com`
-- `modelId` (Optional): e.g. `gemini-embedding-2-preview`, `multimodalembedding@001`
-- `dimensions` (Optional): For `multimodalembedding@001`: `128`, `256`, `512`, or `1408` (default `1408`). For `gemini-embedding-2-preview`: `3072` (default).
+- `modelId` (Optional): e.g. `gemini-embedding-2`, `multimodalembedding@001`
+- `dimensions` (Optional): For `multimodalembedding@001`: `128`, `256`, `512`, or `1408` (default `1408`). For `gemini-embedding-2`: `3072` (default).
 
 <Tabs className="code" groupId="languages">
   <TabItem value="py" label="Python">
@@ -318,7 +318,7 @@ The query below returns the `n` most similar objects to the input image from the
 
 ### Available models
 
-- `gemini-embedding-2-preview` (Vertex AI and Gemini API, added in 1.36.5) — supports text, images, and PDFs; `3072` dimensions
+- `gemini-embedding-2` (Vertex AI and Gemini API, added in 1.36.5) — supports text, images, and PDFs; `3072` dimensions
 - `multimodalembedding@001` (Vertex AI only) — supports text, images, and video; dimensions: `128`, `256`, `512`, `1408`
 
 ## Further resources