diff --git a/docs/modules/chat/getting_started.ipynb b/docs/modules/chat/getting_started.ipynb
index 98662a90..113d652e 100644
--- a/docs/modules/chat/getting_started.ipynb
+++ b/docs/modules/chat/getting_started.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "522686de",
    "metadata": {
     "tags": []
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "62e0dbc3",
    "metadata": {
     "tags": []
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "76a6e7b0-e927-4bfb-a414-1332a4149106",
    "metadata": {
     "tags": []
@@ -68,7 +68,7 @@
        "AIMessage(content=\"J'aime programmer.\", additional_kwargs={})"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
    "metadata": {
     "tags": []
@@ -99,7 +99,7 @@
        "AIMessage(content=\"J'aime programmer.\", additional_kwargs={})"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "2b21fc52-74b6-4950-ab78-45d12c68fb4d",
    "metadata": {
     "tags": []
@@ -131,10 +131,10 @@
     {
      "data": {
       "text/plain": [
-       "LLMResult(generations=[[ChatGeneration(text=\"J'aime programmer.\", generation_info=None, message=AIMessage(content=\"J'aime programmer.\", additional_kwargs={}))], [ChatGeneration(text=\"J'aime l'intelligence artificielle.\", generation_info=None, message=AIMessage(content=\"J'aime l'intelligence artificielle.\", additional_kwargs={}))]], llm_output=None)"
+       "LLMResult(generations=[[ChatGeneration(text=\"J'aime programmer.\", generation_info=None, message=AIMessage(content=\"J'aime programmer.\", additional_kwargs={}))], [ChatGeneration(text=\"J'aime l'intelligence artificielle.\", generation_info=None, message=AIMessage(content=\"J'aime l'intelligence artificielle.\", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}})"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -150,7 +150,39 @@
     "        HumanMessage(content=\"Translate this sentence from English to French. I love artificial intelligence.\")\n",
     "    ],\n",
     "]\n",
-    "chat.generate(batch_messages)"
+    "result = chat.generate(batch_messages)\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2960f50f",
+   "metadata": {},
+   "source": [
+    "You can recover things like token usage from this LLMResult"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a6186bee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'token_usage': {'prompt_tokens': 71,\n",
+       "  'completion_tokens': 18,\n",
+       "  'total_tokens': 89}}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result.llm_output"
    ]
   },
   {
diff --git a/langchain/chat_models/base.py b/langchain/chat_models/base.py
index ffb7c03c..1bf9d4fa 100644
--- a/langchain/chat_models/base.py
+++ b/langchain/chat_models/base.py
@@ -43,19 +43,26 @@ class BaseChatModel(BaseLanguageModel, BaseModel, ABC):
         """
         return callback_manager or get_callback_manager()
 
+    def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
+        return {}
+
     def generate(
         self, messages: List[List[BaseMessage]], stop: Optional[List[str]] = None
     ) -> LLMResult:
         """Top Level call"""
         results = [self._generate(m, stop=stop) for m in messages]
-        return LLMResult(generations=[res.generations for res in results])
+        llm_output = self._combine_llm_outputs([res.llm_output for res in results])
+        generations = [res.generations for res in results]
+        return LLMResult(generations=generations, llm_output=llm_output)
 
     async def agenerate(
         self, messages: List[List[BaseMessage]], stop: Optional[List[str]] = None
     ) -> LLMResult:
         """Top Level call"""
         results = [await self._agenerate(m, stop=stop) for m in messages]
-        return LLMResult(generations=[res.generations for res in results])
+        llm_output = self._combine_llm_outputs([res.llm_output for res in results])
+        generations = [res.generations for res in results]
+        return LLMResult(generations=generations, llm_output=llm_output)
 
     def generate_prompt(
         self, prompts: List[PromptValue], stop: Optional[List[str]] = None
diff --git a/langchain/chat_models/openai.py b/langchain/chat_models/openai.py
index 45a495af..62736b53 100644
--- a/langchain/chat_models/openai.py
+++ b/langchain/chat_models/openai.py
@@ -97,7 +97,8 @@ def _create_chat_result(response: Mapping[str, Any]) -> ChatResult:
         message = _convert_dict_to_message(res["message"])
         gen = ChatGeneration(message=message)
         generations.append(gen)
-    return ChatResult(generations=generations)
+    llm_output = {"token_usage": response["usage"]}
+    return ChatResult(generations=generations, llm_output=llm_output)
 
 
 class ChatOpenAI(BaseChatModel, BaseModel):
@@ -221,6 +222,19 @@ class ChatOpenAI(BaseChatModel, BaseModel):
 
         return _completion_with_retry(**kwargs)
 
+    def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
+        overall_token_usage: dict = {}
+        for output in llm_outputs:
+            if output is None:
+                raise ValueError("Should always be something for OpenAI.")
+            token_usage = output["token_usage"]
+            for k, v in token_usage.items():
+                if k in overall_token_usage:
+                    overall_token_usage[k] += v
+                else:
+                    overall_token_usage[k] = v
+        return {"token_usage": overall_token_usage}
+
     def _generate(
         self, messages: List[BaseMessage], stop: Optional[List[str]] = None
     ) -> ChatResult: