Set `stream: true` and tokens arrive as Server-Sent Events. The protocol is the same as OpenAI's, so an existing stream handler works unchanged.

## Stream a response

```python
stream = client.chat.completions.create(
    model="deepseek/deepseek-v4-pro",
    messages=[{"role": "user", "content": "Count to ten."}],
    stream=True,
)

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

The stream ends with a `data: [DONE]` line, exactly like OpenAI.

## Get token counts while streaming

Add `"stream_options": {"include_usage": true}` to the request body to get a final chunk with the token counts for the whole request.

```python
stream = client.chat.completions.create(
    model="deepseek/deepseek-v4-pro",
    messages=[{"role": "user", "content": "Count to ten."}],
    stream=True,
    stream_options={"include_usage": True},
)
```

## Streaming and spend limits

[Budgets](/docs/budgets) are checked when a request starts, not while it runs. A stream in progress is never cut off, so a request that starts just under a limit can finish and push usage slightly over it. The next request is refused.

## Reasoning models

Models that return their thinking send it separately from the answer in the stream. See [Reasoning & tracing](/docs/reasoning-and-tracing).