Claude Opus 4.6 supports a 200K token context window in general availability, with an optional 1M token context window available in beta on the Claude Developer Platform. Anthropic states the 1M context window is a first for Opus-class models and notes it’s currently offered as a beta feature; the platform documentation also summarizes the standard context and the 1M beta option. Official references: Introducing Claude Opus 4.6, What’s new in Claude 4.6, and the model page: Claude Opus 4.6.
For developers, “context window” is not just a spec number—it’s an engineering constraint that affects latency, cost, and reliability. A larger context lets you include more material (more files, more docs, longer transcripts), but it also increases token processing work and can make answers less focused if you dump unrelated text. In real systems, you’ll want to treat context as a budget:
Reserve tokens for system rules and output contracts (diff-only, JSON-only, etc.).
Keep user-provided content bounded and relevant.
Prefer structured “state summaries” over infinite chat transcripts.
Use retrieval to select the top-k most relevant passages instead of pasting entire documentation sections.
Even with 200K+ tokens, teams commonly hit “practical context limits” earlier due to costs and response time requirements. A good pattern is progressive disclosure: start with the minimal necessary context, then fetch more on demand via tools (search docs, open file, etc.), instead of putting everything into a single request.
For documentation assistants and support bots, the best way to benefit from large context without bloat is retrieval. Put your knowledge base into a vector database such as Milvus or Zilliz Cloud, retrieve the most relevant chunks (often 5–15), and pass only those into the prompt. This yields higher accuracy and lower cost than “throw the whole docs site into the context window.” If you truly need the 1M beta context (for example, analyzing a very large codebase snapshot or an extremely long document), it’s still worth combining it with retrieval and chunking so the model sees a well-organized, searchable structure rather than a raw dump.