What Are Tokens?
Tokens are the fundamental units LLMs process. A token is roughly 4 characters or 0.75 words. "Hello world" = 2 tokens. Code has more tokens per line due to special characters. A 1000-line Python file might be 5,000-8,000 tokens. Understanding token counts helps you manage AI tool usage effectively.
Context Window Sizes in 2026
Claude 3.5 Sonnet: 200K tokens (~150K words). GPT-4 Turbo: 128K tokens. Gemini 1.5 Pro: 1M+ tokens. DeepSeek R1: 128K tokens. Larger windows let you include more code, but cost increases linearly with input size. Most coding tasks work fine within 32K tokens.
Impact on Development
Short context: AI forgets earlier instructions, generates inconsistent code. Long context: include entire files, multiple files, or full project context. For code generation, the sweet spot is providing the relevant file plus related interfaces/types — not your entire codebase.
Managing Context Effectively
Include only relevant code — not everything. Use file references instead of full content when possible. Summarize large files before including them. Use RAG to retrieve relevant chunks instead of brute-forcing everything into context. Quality of context matters more than quantity.
Cost Implications
Input tokens are cheaper than output tokens, but large contexts add up. Sending 100K tokens at $0.003/1K = $0.30 per request. At 100 requests/day, that's $30/day. Use smaller contexts for simple tasks and reserve large contexts for complex multi-file operations.
Conclusion
Context windows are expanding rapidly, but bigger isn't always better. Focus on providing high-quality, relevant context rather than dumping everything in. Combine large context windows with RAG for the best results on codebase-wide tasks.