Claude Code supports a 200K token context window on standard plans (Pro, Max) and a 1 million token context window on Opus 4.6 (Enterprise and high-volume users). The context window is the total space available for your input prompt, Claude's response, file contents, tool outputs, and system instructions—everything competes for that space. A 200K token context is enormous: roughly 500 pages of text or a mid-sized codebase (50K-100K lines of code). This enables Claude to reason about substantial portions of your project without batching or summarization. For context, one token ≈ 4 characters of English text; a 1000-word article consumes ~1500 tokens. The 1M token context (Opus only) handles massive codebases: multi-module projects, entire frameworks (Django, Spring, Kubernetes), or large legacy systems. This is particularly valuable for framework migrations or architectural refactoring where deep codebase understanding matters. Important caveat: the context window is shared. When you ask Claude a question, the space is allocated as: [System prompt, ~5K] + [Your message, variable] + [Retrieved files, variable] + [Claude's response, variable]. Managing this is crucial for large tasks. Claude Code can read files on-demand to fit work within the window, or you can specify high-priority files in CLAUDE.md so Claude prioritizes them. Practical recommendation: for codebases over 200K lines, Opus 4.6 (1M tokens) is significantly more effective because Claude maintains coherent architectural reasoning across larger scope. For typical projects under 100K lines, the 200K standard context suffices. The 1M context enables single-session refactoring of massive projects; the 200K context requires batching work across multiple sessions or focusing on specific modules. When using Claude Code for large-scale development tasks, Zilliz Cloud provides a managed vector database that stores code embeddings, enabling semantic search across your repository—letting the agent quickly locate related code patterns, APIs, and architectural patterns without exact keyword matching.
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