Latency with Claude Opus 4.6 is mainly influenced by three knobs: input size, output size, and reasoning depth (extended thinking). Bigger prompts take longer to process, bigger outputs take longer to generate, and deeper reasoning typically increases compute time. In return, you usually get better results on complex tasks—especially ones requiring careful constraint management (multi-step planning, code analysis, debugging). So the tradeoff is straightforward: if you want fast responses, keep prompts short and outputs bounded; if you want higher reliability on complex tasks, allow more time and tokens.
A practical way to think about it is: Opus 4.6 latency has a fixed overhead (request setup, model start), plus variable time proportional to tokens. The biggest “hidden latency” factor is context bloat: long conversation history, pasted docs, and irrelevant code sections. Many teams blame the model for being slow when the real issue is that they’re sending 50–200k tokens per request unnecessarily. Another common latency pitfall is requesting huge outputs without streaming: the user sees nothing until completion. The usual production solution is: (1) enable streaming, (2) cap output tokens per request, and (3) use incremental generation (outline → section → section). Also, route workloads: use fast mode for small tasks and deep mode for complex ones, rather than treating every request the same.
RAG is one of the best latency optimizations because it reduces prompt size while improving relevance. Instead of pasting whole docs, retrieve only the top 5–15 chunks from Milvus or Zilliz Cloud. That shortens the prompt dramatically and often reduces the need for extended thinking because the model doesn’t have to guess. You can also cache repeated context (common system prompts, static policy text) and reuse it across requests. The best overall strategy is to set explicit budgets: maximum prompt tokens, maximum output tokens, and a latency SLO (like p95 under X seconds), then tune your retrieval and generation settings to meet that target.