Streaming

Supported and recommended for anything user-facing:

{"model": "cf-chat", "messages": [...], "stream": true}

stream = client.chat.completions.create(
    model="cf-chat",
    messages=[{"role": "user", "content": "Write a haiku about GPUs."}],
    stream=True,
    stream_options={"include_usage": True},
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

With stream_options.include_usage, the final chunk carries prompt and completion token counts.

What the example prints

The loop above prints each content delta as it arrives, so the answer appears fragment by fragment:

Thousands of cores hum
fanning out to paint the dark,
green light in the case

The same response as it arrived on the wire, one chunk per delta (from a real run; note chunks split mid-word):

"\n\nThousands" " of" " cores" " hum" "\n" "f" "anning" " out" " to"
" paint" " the" " dark" "," "\n" "green" " light" " in" " the" " case"

In that run, those 19 content chunks were preceded by 201 reasoning_content chunks that the loop deliberately skips.

The first chunk is thinking, not the answer

One caveat if you are driving a UI. The first chunk arrives fast (about 0.4 s in the run above), but it is reasoning, not the answer: the first chunk of actual content lands only once the model has finished thinking (2.2 s in that run; longer when the model thinks harder). So either render the reasoning as it streams, or show a thinking indicator until content starts, otherwise the user watches an empty box.