Understanding Context Windows: Why 128K vs. 1M Tokens Matters
Context window size determines how much text a model can "see" at once. Here's how it affects RAG, codebase work, and long documents.
A model's context window is the maximum amount of text (in tokens) it can take as input in one go. That number directly affects what you can build.
Smaller windows (e.g. 32K–128K)
Fine for short conversations, single files, and focused tasks. Many coding specialists use 32K–64K. Models like GPT-4o and Claude offer 128K, which is enough for substantial documents and multi-file snippets. You'll hit limits when you need to send entire repos or very long reports.
Larger windows (200K–1M+)
Claude and others offer 200K tokens. Gemini 1.5 Pro goes up to 1 million tokens. Large windows help when you're doing retrieval-augmented generation (RAG) over big corpora, analyzing full codebases, or summarizing long transcripts and manuals. The tradeoff is cost and sometimes latency—more context means more compute.
What to look for on this platform
Each model's card shows its context window. Use filters and comparison views to see which models fit your "how much context do I need?" budget.
