Introduction
Context windows determine how much past code an AI model can 'see' at a time. For coders, especially those using tools like GPT‑5 Codex or Claude Sonnet inside IDEs, the size of this window directly affects completion accuracy and refactoring performance.
What Is a Context Window?
A context window is the maximum number of tokens (words, symbols, or characters in encoded form) that a model can process in one input-output cycle. Think of it like a whiteboard — the larger the space, the more of your code and comments can fit for the AI to review at once.
Tokens are the basic unit of LLM input; one token may represent a few characters or a word. Larger windows let you push more complete modules, extensive comments, or multiple files for the AI to reference.
LLM Memory in Coding Workflows
When you ask an LLM to continue your code, it doesn’t have real memory — it only sees what’s in the current context window. If your code exceeds that size, older parts will scroll out of view.
Effects on workflows:
- Code Completion: Larger windows mean the model can consider variables or functions declared far earlier.
- Debugging: Maintains visibility into the original bug context.
- Refactoring: Keeps multi-file dependencies in scope.
Context Window Sizes by Model
Below is a snapshot of leading models and their maximum token capacities:
High-Capacity (≥1M tokens)
- gemini‑2.5‑pro — 1,000,000 tokens
- gemini‑2.5‑flash — 1,000,000 tokens
Upper-Mid Tier (256k tokens)
- grok‑code‑fast‑1 — 256,000 tokens
- grok‑4 — 256,000 tokens
- qwen3‑max — 256,000 tokens
Standard Large (200k tokens)
- claude‑sonnet‑4 — 200,000 tokens
- claude‑sonnet‑4‑5‑20250929 — 200,000 tokens
- claude‑haiku‑4‑5‑20251001 — 200,000 tokens
- glm‑4.6 — 200,000 tokens
- gpt‑5 — 200,000 tokens
- gpt‑5‑codex — 200,000 tokens
Mid Tier (~128k tokens)
- DeepSeek r1 — 128,000 tokens
- DeepSeek v3 — 128,000 tokens
- deepseek‑v3.1 — 128,000 tokens
- glm‑4.5 — 128,000 tokens
Niche (~131k tokens)
- deepseek‑v3.2‑exp — 131,000 tokens
Demo: Code Completion & Refactoring
To illustrate how context size affects development, let's simulate two scenarios.
Test Setup
We feed the same large project into various LLMs. The project contains multiple modules, lengthy comments, and spread-out dependencies.
Completion Example
If your function references a variable defined 5,000 lines earlier:
- Small Context: The definition is no longer visible; AI guesses or produces errors.
- Large Context: The declaration remains in the window, producing correct references.
Refactoring Example
When moving functions across files:
- Small Context: The AI may forget related helper functions, breaking code.
- Large Context: All relevant code is in view; the AI can update imports and calls accurately.
Choosing the Right Model
Match the context size to your needs:
- Mega Projects: Use ≥1M token models (Gemini 2.5 Pro/Flash)
- Complex Microservices with deep cross-file references: Aim for ≥256k
- General Enterprise Apps: 200k is often sufficient
Trade-offs include:
- Larger contexts can be slower
- Costs may rise per request
Practical Tips
- Chunking: Break code logically if using smaller contexts.
- Summarization: Add short intent notes for sections that might drop out.
- Prompt Engineering: Build scripts to include critical files before the main task.
Future Trends
Models are pushing towards multi‑million token contexts. Expect smarter memory management, possibly selective context refresh.
Conclusion
Large context windows enable accurate, coherent multi‑file coding with LLMs. Choose size based on project scope, while balancing speed and cost.