所有具备文本输出能力的模型（Chat、Image Understanding 等）都支持 streaming output。具备图像输出能力的模型（Image Generation）不支持该功能。

Streaming output 使用 Server-Sent Events（SSE），使服务器可以在 event stream 中返回增量内容。

Streaming response 可以提供实时反馈，并允许文本在生成时立即显示，从而增强用户交互体验。

要启用 streaming，必须在请求中设置 "stream": true。

import os

from xai_sdk import Client
from xai_sdk.chat import user, system

client = Client(
    api_key=os.getenv('XAI_API_KEY'),
    timeout=3600, # Override default timeout with longer timeout for reasoning models
)

chat = client.chat.create(model="grok-4.5")
chat.append(
    system("You are Grok, a helpful and maximally truthful AI built by xAI."),
)
chat.append(
    user("Explain how neural networks learn in two sentences.")
)

for response, chunk in chat.stream():
    print(chunk.content, end="", flush=True) # Each chunk's content
    print(response.content, end="", flush=True) # The response object auto-accumulates the chunks

print(response.content) # The full response

import os
import httpx
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("XAI_API_KEY"),
    base_url="https://api.x.ai/v1",
    timeout=httpx.Timeout(3600.0) # Timeout after 3600s for reasoning models
)

stream = client.chat.completions.create(
    model="grok-4.5",
    messages=[
        {"role": "system", "content": "You are Grok, a helpful and maximally truthful AI built by xAI."},
        {"role": "user", "content": "Explain how neural networks learn in two sentences."},
    ],
    stream=True # Set streaming here
)

for chunk in stream:
    print(chunk.choices[0].delta.content, end="", flush=True)

import OpenAI from "openai";
const openai = new OpenAI({
    apiKey: "<api key>",
    baseURL: "https://api.x.ai/v1",
    timeout: 360000, // Timeout after 3600s for reasoning models
});

const stream = await openai.chat.completions.create({
    model: "grok-4.5",
    messages: [
        { role: "system", content: "You are Grok, a helpful and maximally truthful AI built by xAI." },
        {
            role: "user",
            content: "Explain how neural networks learn in two sentences.",
        }
    ],
    stream: true
});

for await (const chunk of stream) {
    console.log(chunk.choices[0].delta.content);
}

import { xai } from '@ai-sdk/xai';
import { streamText } from 'ai';

const result = streamText({
  model: xai.responses('grok-4.5'),
  system:
    "You are Grok, a helpful and maximally truthful AI built by xAI.",
  prompt: 'Explain how neural networks learn in two sentences.',
});

for await (const chunk of result.textStream) {
  process.stdout.write(chunk);
}

curl https://api.x.ai/v1/chat/completions \\
-H "Content-Type: application/json" \\
-H "Authorization: Bearer $XAI_API_KEY" \\
-m 3600 \\
-d '{
    "messages": [
        {
            "role": "system",
            "content": "You are Grok, a helpful and maximally truthful AI built by xAI."
        },
        {
            "role": "user",
            "content": "Explain how neural networks learn in two sentences."
        }
    ],
    "model": "grok-4.5",
    "stream": true
}'

你将获得类似下面的 event stream：

data: {
    "id":"<completion_id>","object":"chat.completion.chunk","created":<creation_time>,
    "model":"grok-4.5",
    "choices":[{"index":0,"delta":{"content":"Ah","role":"assistant"}}],
    "usage":{"prompt_tokens":41,"completion_tokens":1,"total_tokens":42,
    "prompt_tokens_details":{"text_tokens":41,"audio_tokens":0,"image_tokens":0,"cached_tokens":0}},
    "system_fingerprint":"fp_xxxxxxxxxx"
}

data: {
    "id":"<completion_id>","object":"chat.completion.chunk","created":<creation_time>,
    "model":"grok-4.5",
    "choices":[{"index":0,"delta":{"content":",","role":"assistant"}}],
    "usage":{"prompt_tokens":41,"completion_tokens":2,"total_tokens":43,
    "prompt_tokens_details":{"text_tokens":41,"audio_tokens":0,"image_tokens":0,"cached_tokens":0}},
    "system_fingerprint":"fp_xxxxxxxxxx"
}

data: [DONE]

建议使用客户端 SDK 解析 event stream。

Python/Javascript 中的 streaming response 示例：

Neural networks learn by adjusting connection weights to minimize prediction error. Through backpropagation, they propagate gradients backward through layers so each weight updates in the direction that improves accuracy on training data.