diff --git a/pages/generative-apis/how-to/query-vision-models.mdx b/pages/generative-apis/how-to/query-vision-models.mdx
index 1bac3e7330..dd123d1602 100644
--- a/pages/generative-apis/how-to/query-vision-models.mdx
+++ b/pages/generative-apis/how-to/query-vision-models.mdx
@@ -79,9 +79,9 @@ client = OpenAI(
 You can now create a chat completion:
 
 ```python
-# Create a chat completion using the 'pixtral-12b-2409' model
+# Create a chat completion using the 'mistral-small-3.2-24b-instruct-2506' model
 response = client.chat.completions.create(
-    model="pixtral-12b-2409",
+    model="mistral-small-3.2-24b-instruct-2506",
     messages=[
     {
         "role": "user",
@@ -109,57 +109,43 @@ A conversation style may include a default system prompt. You may set this promp
 [
   {
   	"role": "system",
-  	"content": "You are Xavier Niel."
+  	"content": "You are an expert developer."
   }
 ]
 ```
 
-### Passing images to Pixtral
+### Passing images to the vision model
 
 1. **Image URLs**: If the image is available online, you can just include the image URL in your request as demonstrated above. This approach is simple and does not require any encoding.
 2. **Base64 encoded**: image Base64 encoding is a standard way to transform binary data, like images, into a text format, making it easier to transmit over the internet.
 
-To encode Base64 images in Python, you first need to install `Pillow` library:
-
-```bash
-pip install pillow
-```
-
-Then, the following Python code sample shows you how to encode an image in Base64 format and pass it to a request payload for the Chat Completions API:
+The following code sample shows you how to encode an image in Base64 format and pass it to a request payload for the Chat Completions API, with an [example image](https://genapi-documentation-assets.s3.fr-par.scw.cloud/scaleway-illustration-robot.jpg).
 
 ```python
 import base64
-from io import BytesIO
-from PIL import Image
-
-def encode_image(img):
-    buffered = BytesIO()
-    img.save(buffered, format="JPEG")
-    encoded_string = base64.b64encode(buffered.getvalue()).decode("utf-8")
-    return encoded_string
-
-img = Image.open("path_to_your_image.jpg")
-base64_img = encode_image(img)
-
-payload = {
-    "messages": [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "What is this image?"
-                },
-                {
-                    "type": "image_url",
-                    "image_url": {
-                        "url": f"data:image/jpeg;base64,{base64_img}"
-                    }
-                }
-            ]
-        }
-    ]
-}
+
+with open("path/to/your/image.png", "rb") as file:
+    image_content = file.read()
+    encoded_image = base64.b64encode(image_content).decode("utf-8")
+
+response = client.chat.completions.create(
+    model="mistral-small-3.2-24b-instruct-2506",
+    messages=[
+      {
+          "role": "user",
+          "content": [
+              {"type": "text", "text": "What is this image?"},
+              {"type": "image_url",
+                  "image_url": {
+                  "url": f"data:image/jpeg;base64,{encoded_image}"}
+              },
+          ]
+      }
+    ],
+    temperature=0.7,
+    max_tokens=2048,
+    top_p=0.9
+)
 
 ```
 
@@ -191,15 +177,15 @@ client = OpenAI(
     api_key="<SCW_API_KEY>"  # Your unique API key from Scaleway
 )
 response = client.chat.completions.create(
-model="pixtral-12b-2409",
-messages=[{
-    "role": "user", 
-    "content": [
-        {"type": "text", "text": "What is this image?"},
-        {"type": "image_url", "image_url": {"url": "https://picsum.photos/id/32/512/512"}},
-    ]
-}],
-stream=True,
+  model="mistral-small-3.2-24b-instruct-2506",
+  messages=[{
+      "role": "user", 
+      "content": [
+          {"type": "text", "text": "What is this image?"},
+          {"type": "image_url", "image_url": {"url": "https://picsum.photos/id/32/512/512"}},
+      ]
+  }],
+  stream=True,
 )
 
 for chunk in response:
@@ -224,7 +210,7 @@ client = AsyncOpenAI(
 
 async def main():
     stream = await client.chat.completions.create(
-        model="pixtral-12b-2409",
+        model="mistral-small-3.2-24b-instruct-2506",
         messages=[{
             "role": "user", 
             "content": [
@@ -244,7 +230,7 @@ asyncio.run(main())
 ## Frequently Asked Questions
 
 #### Is there a limit to the size of each image?
-The only limitation is in context window (1 token for each 16x16 pixel).
+The only limitation are the tokens context window and the maximum resolution supported by the model (images will be automatically downscaled to fit within maximum resolution). Refer to our [model catalog](/managed-inference/reference-content/model-catalog/#mistral-small-31-24b-instruct-2503) for more information about supported formats and token dimensions for each model.
 
 #### What is the maximum amount of images per conversation?
 Each conversation can handle up to 12 images (per request). Attempting to add a 13th image will result in a 400 Bad Request error.