Streaming Responses - Neosantara Docs

Streaming on the /v1/chat/completions endpoint enables your applications to display text tokens as soon as they are produced by the AI model, minimizing perceived latency for chat and terminal interfaces.

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://api.neosantara.xyz/v1",
    api_key=os.environ["NEOSANTARA_API_KEY"]
)

stream = client.chat.completions.create(
    model="deepseek-v4.1-flash",
    messages=[{"role": "user", "content": "Outline a microservices architecture guide."}],
    stream=True
)

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

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.neosantara.xyz/v1",
  apiKey: process.env.NEOSANTARA_API_KEY,
});

const stream = await client.chat.completions.create({
  model: "deepseek-v4.1-flash",
  messages: [{ role: "user", content: "Explain SOLID principles concisely." }],
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || "");
}
console.log();

curl -N -X POST https://api.neosantara.xyz/v1/chat/completions \
  -H "Authorization: Bearer $NEOSANTARA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4.1-flash",
    "messages": [{"role": "user", "content": "Hello"}],
    "stream": true
  }'

Event Stream Format

Each chunk arrives as an SSE event in data: <JSON> format:

data: {"id":"chatcmpl-abc","object":"chat.completion.chunk","created":1741764000,"model":"deepseek-v4.1-flash","choices":[{"index":0,"delta":{"content":"Hello"},"finish_reason":null}]}

When generation finishes, the gateway transmits the standard completion signal:

data: [DONE]

Tracking Token Usage During Streams

Pass stream_options to receive the final token accounting block in the trailing chunk:

stream = client.chat.completions.create(
    model="deepseek-v4.1-flash",
    messages=[{"role": "user", "content": "Hello"}],
    stream=True,
    stream_options={"include_usage": True}
)

for chunk in stream:
    if chunk.usage:
        print(f"\nTotal tokens: {chunk.usage.total_tokens}")
    else:
        print(chunk.choices[0].delta.content or "", end="", flush=True)

Next Steps

Task	Guide
Invoke External Functions	Function Calling
Validated JSON Outputs	Structured Outputs
Extract Model Reasoning	Model Reasoning

Related topics

Chat Completions Anthropic Streaming Model Reasoning Rate Limits & Throughput

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