diff --git a/site/en/tutorials/python_quickstart.ipynb b/site/en/tutorials/python_quickstart.ipynb
index a3be1f52f..167e74369 100644
--- a/site/en/tutorials/python_quickstart.ipynb
+++ b/site/en/tutorials/python_quickstart.ipynb
@@ -1034,6 +1034,64 @@
         "  display(to_markdown(f'**{message.role}**: {message.parts[0].text}'))"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AEgVOYu0pAr4"
+      },
+      "source": [
+        "## Count tokens\n",
+        "\n",
+        "Large language models have a context window, and the context length is often measured in terms of the **number of tokens**. With the Gemini API, you can determine the number of tokens per any `glm.Content` object. In the simplest case, you can pass a query string to the `GenerativeModel.count_tokens` method as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eLjBmPCLpElk"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "total_tokens: 7"
+          ]
+        }
+      ],
+      "source": [
+        "model.count_tokens(\"What is the meaning of life?\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oM2_U8pmpHQA"
+      },
+      "source": [
+        "Similarly, you can check `token_count` for your `ChatSession`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "i0MUU4BZpG4_"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "total_tokens: 501"
+          ]
+        }
+      ],
+      "source": [
+        "model.count_tokens(chat.history)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {