To stream very large outputs from Opus 4.6, you use the Claude API’s streaming capability so tokens arrive incrementally rather than waiting for the entire response to finish. Streaming matters a lot when outputs can be huge: it improves user-perceived latency, allows you to render partial results in the UI, and lets you implement early stopping if the output becomes off-track.
Engineering-wise, you should design streaming as a first-class protocol: handle network interruptions, implement resumability where appropriate, and make your UI tolerant of partial content. Also enforce practical limits. Even though 128K output tokens are supported, most products shouldn’t allow that by default; cap output tokens per request, and for large tasks prefer chunked deliverables (e.g., generate an outline first, then sections, then appendices) so users can steer the result. For structured outputs, stream into a buffer and validate only when complete, or stream as NDJSON-like records if your consumer can parse incrementally.
If your large outputs are based on a knowledge base, avoid “generate everything from scratch.” Retrieve only the necessary facts from Milvus or managed Zilliz Cloud and ask Opus 4.6 to produce targeted sections. This keeps streamed content relevant and reduces the risk of long, meandering outputs that cost a lot and don’t help the user.