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*   [Model I/O](/v0.1/docs/modules/model_io/)
*   [Chat Models](/v0.1/docs/modules/model_io/chat/)
*   Response metadata

On this page

Response metadata
=================

Many model providers include some metadata in their chat generation responses. This metadata can be accessed via the `AIMessage.response_metadata` attribute. Depending on the model provider and model configuration, this can contain information like [token counts](/v0.1/docs/modules/model_io/chat/token_usage_tracking/) and more.

Here’s what the response metadata looks like for a few different providers:

OpenAI[​](#openai "Direct link to OpenAI")
------------------------------------------

    import { ChatOpenAI } from "@langchain/openai";const chatModel = new ChatOpenAI({ model: "gpt-4-turbo" });const message = await chatModel.invoke([  ["human", "What's the oldest known example of cuneiform"],]);console.log(message.response_metadata);

    {  tokenUsage: { completionTokens: 164, promptTokens: 17, totalTokens: 181 },  finish_reason: "stop"}

Anthropic[​](#anthropic "Direct link to Anthropic")
---------------------------------------------------

    import { ChatAnthropic } from "@langchain/anthropic";const chatModel = new ChatAnthropic({ model: "claude-3-sonnet-20240229" });const message = await chatModel.invoke([  ["human", "What's the oldest known example of cuneiform"],]);console.log(message.response_metadata);

    {  id: "msg_01K8kC9wskG6qsSGRmY7b3kj",  model: "claude-3-sonnet-20240229",  stop_sequence: null,  usage: { input_tokens: 17, output_tokens: 355 },  stop_reason: "end_turn"}

Google VertexAI[​](#google-vertexai "Direct link to Google VertexAI")
---------------------------------------------------------------------

    import { ChatVertexAI } from "@langchain/google-vertexai-web";const chatModel = new ChatVertexAI({ model: "gemini-pro" });const message = await chatModel.invoke([  ["human", "What's the oldest known example of cuneiform"],]);console.log(message.response_metadata);

    {  usage_metadata: {    prompt_token_count: undefined,    candidates_token_count: undefined,    total_token_count: undefined  },  safety_ratings: [    {      category: "HARM_CATEGORY_HATE_SPEECH",      probability: "NEGLIGIBLE",      probability_score: 0.027480692,      severity: "HARM_SEVERITY_NEGLIGIBLE",      severity_score: 0.073430054    },    {      category: "HARM_CATEGORY_DANGEROUS_CONTENT",      probability: "NEGLIGIBLE",      probability_score: 0.055412795,      severity: "HARM_SEVERITY_NEGLIGIBLE",      severity_score: 0.112405084    },    {      category: "HARM_CATEGORY_HARASSMENT",      probability: "NEGLIGIBLE",      probability_score: 0.055720285,      severity: "HARM_SEVERITY_NEGLIGIBLE",      severity_score: 0.020844316    },    {      category: "HARM_CATEGORY_SEXUALLY_EXPLICIT",      probability: "NEGLIGIBLE",      probability_score: 0.05223086,      severity: "HARM_SEVERITY_NEGLIGIBLE",      severity_score: 0.14891148    }  ],  finish_reason: undefined}

MistralAI[​](#mistralai "Direct link to MistralAI")
---------------------------------------------------

    import { ChatMistralAI } from "@langchain/mistralai";const chatModel = new ChatMistralAI({ model: "mistral-tiny" });const message = await chatModel.invoke([  ["human", "What's the oldest known example of cuneiform"],]);console.log(message.response_metadata);

    {  tokenUsage: { completionTokens: 166, promptTokens: 19, totalTokens: 185 },  finish_reason: "stop"}

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*   [OpenAI](#openai)
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