You stream long outputs by using the Messages API in streaming mode, which delivers incremental updates over Server-Sent Events (SSE). Instead of waiting for the full response, you receive a sequence of events (for example, message start, content deltas, message stop) that you can render to the user as tokens arrive. This dramatically improves perceived latency for long outputs: users see progress immediately, and you can also stop early if the response is going off track. Streaming is essential when you let Opus 4.6 generate multi-thousand-token outputs, because it keeps the UI responsive and provides a natural place to add “cancel” and “regenerate” controls.
In practice, streaming is an application design problem as much as an API flag. You’ll want to implement three pieces: (1) an SSE client that parses event frames, (2) a renderer that appends text deltas to a buffer, and (3) a finalizer that commits the completed message to storage. A good UX pattern is to show the response as it streams, but also capture structured checkpoints. For example, if you ask the model to generate Markdown with ## Summary, ## Steps, and ## Risks, you can detect when it has finished the Summary section and display it immediately, even as later sections continue streaming. Another useful pattern is to stream the main answer but require a short final “verification block” at the end (commands to run, expected outputs). That makes the response actionable without forcing you to wait for every last token.
Streaming works especially well with RAG. Retrieve chunks from Milvus or Zilliz Cloud, start streaming the answer, and log which chunk IDs were used. If you detect that the model is producing content that isn’t supported by retrieved chunks (for example, missing citations), you can stop the stream and re-prompt with stricter instructions. This turns streaming into a quality control mechanism, not just a UI improvement. In production systems, that can reduce hallucinations and save cost by cutting off bad generations early.