> ## Documentation Index
> Fetch the complete documentation index at: https://opencompress.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Streaming

> Real-time streaming responses with server-sent events.

## How streaming works

OpenCompress supports streaming via server-sent events (SSE), identical to the OpenAI API. Set `"stream": true` in your request body.

**Important:** Compression happens *before* the stream starts. The compression step adds a small fixed latency (\~100-300ms), but once streaming begins, tokens arrive at the same speed as a direct API call.

```
Compression (100-300ms) → Stream starts → Tokens arrive at full speed
```

## Request

```bash theme={null}
curl https://www.opencompress.ai/api/v1/chat/completions \
  -H "Authorization: Bearer sk-occ-your-key-here" \
  -H "Content-Type: application/json" \
  -N \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Tell me a story."}],
    "stream": true
  }'
```

## Response format

Each SSE event contains a JSON chunk:

```
data: {"id":"gen-abc","object":"chat.completion.chunk","created":1772341560,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":"Once"},"finish_reason":null}]}

data: {"id":"gen-abc","object":"chat.completion.chunk","created":1772341560,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":" upon"},"finish_reason":null}]}

data: {"id":"gen-abc","object":"chat.completion.chunk","created":1772341560,"model":"gpt-4o","choices":[{"index":0,"delta":{},"finish_reason":"stop"}],"usage":{"prompt_tokens":12,"completion_tokens":85,"total_tokens":97}}

data: [DONE]
```

The final chunk includes `usage` data with token counts. Billing is calculated after the stream completes.

## SDK examples

<CodeGroup>
  ```python Python theme={null}
  from openai import OpenAI

  client = OpenAI(
      base_url="https://www.opencompress.ai/api/v1",
      api_key="sk-occ-your-key-here",
  )

  stream = client.chat.completions.create(
      model="gpt-4o",
      messages=[{"role": "user", "content": "Tell me a story."}],
      stream=True,
  )

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

  ```typescript TypeScript theme={null}
  import OpenAI from "openai";

  const client = new OpenAI({
    baseURL: "https://www.opencompress.ai/api/v1",
    apiKey: "sk-occ-your-key-here",
  });

  const stream = await client.chat.completions.create({
    model: "gpt-4o",
    messages: [{ role: "user", content: "Tell me a story." }],
    stream: true,
  });

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content;
    if (content) process.stdout.write(content);
  }
  ```
</CodeGroup>