diff --git a/examples/o-series/o3o4-mini_prompting_guide.ipynb b/examples/o-series/o3o4-mini_prompting_guide.ipynb
index 78a5f936d3..991ad10882 100644
--- a/examples/o-series/o3o4-mini_prompting_guide.ipynb
+++ b/examples/o-series/o3o4-mini_prompting_guide.ipynb
@@ -176,10 +176,28 @@
     "# Responses API\n",
     "\n",
     "### Reasoning Items for Better Performance\n",
-    "We’ve released a [cookbook](https://cookbook.openai.com/examples/responses_api/reasoning_items) detailing the benefits of using the responses API. It is worth restating a few of the main points in this guide as well. o3/o4-mini are both trained with its internal reasoning persisted between toolcalls within a single turn. Persisting these reasoning items between toolcalls during inference will therefore lead to higher intelligence and performance in the form of better decision in when and how a tool gets called. Responses allow you to persist these reasoning items (maintained either by us or yourself through encrypted content if you do not want us to handle state-management) while Chat Completion doesn’t. Switching to the responses API and allowing the model access to reasoning items between function calls is the easiest way to squeeze out as much performance as possible for function calls. Here is an the example in the cookbook, reproduced for convenience, showing how you can pass back the reasoning item using `encrypted_content` in a way which we do not retain any state on our end\n",
-    "\n",
-    "```\n",
+    "We’ve released a [cookbook](https://cookbook.openai.com/examples/responses_api/reasoning_items) detailing the benefits of using the responses API. It is worth restating a few of the main points in this guide as well. o3/o4-mini are both trained with its internal reasoning persisted between toolcalls within a single turn. Persisting these reasoning items between toolcalls during inference will therefore lead to higher intelligence and performance in the form of better decision in when and how a tool gets called. Responses allow you to persist these reasoning items (maintained either by us or yourself through encrypted content if you do not want us to handle state-management) while Chat Completion doesn’t. Switching to the responses API and allowing the model access to reasoning items between function calls is the easiest way to squeeze out as much performance as possible for function calls. Here is an the example in the cookbook, reproduced for convenience, showing how you can pass back the reasoning item using `encrypted_content` in a way which we do not retain any state on our end:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The current temperature in Paris is about 18.8 °C.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from openai import OpenAI\n",
     "import requests\n",
+    "import json\n",
+    "client = OpenAI()\n",
+    "\n",
     "\n",
     "def get_weather(latitude, longitude):\n",
     "    response = requests.get(f\"https://api.open-meteo.com/v1/forecast?latitude={latitude}&longitude={longitude}&current=temperature_2m,wind_speed_10m&hourly=temperature_2m,relative_humidity_2m,wind_speed_10m\")\n",
@@ -206,14 +224,37 @@
     "context = [{\"role\": \"user\", \"content\": \"What's the weather like in Paris today?\"}]\n",
     "\n",
     "response = client.responses.create(\n",
-    "    model=\"o4-mini\",\n",
+    "    model=\"o3\",\n",
     "    input=context,\n",
     "    tools=tools,\n",
+    "    store=False,\n",
+    "    include=[\"reasoning.encrypted_content\"] # Encrypted chain of thought is passed back in the response\n",
     ")\n",
     "\n",
     "\n",
-    "response.output\n",
-    "```\n"
+    "context += response.output # Add the response to the context (including the encrypted chain of thought)\n",
+    "tool_call = response.output[1]\n",
+    "args = json.loads(tool_call.arguments)\n",
+    "\n",
+    "\n",
+    "\n",
+    "result = get_weather(args[\"latitude\"], args[\"longitude\"])\n",
+    "\n",
+    "context.append({                               \n",
+    "    \"type\": \"function_call_output\",\n",
+    "    \"call_id\": tool_call.call_id,\n",
+    "    \"output\": str(result)\n",
+    "})\n",
+    "\n",
+    "response_2 = client.responses.create(\n",
+    "    model=\"o3\",\n",
+    "    input=context,\n",
+    "    tools=tools,\n",
+    "    store=False,\n",
+    "    include=[\"reasoning.encrypted_content\"]\n",
+    ")\n",
+    "\n",
+    "print(response_2.output_text)"
    ]
   },
   {