What is a context window?
A context window is the maximum amount of text an AI model can process at once. Think of it as the model’s “working memory” - it determines how much of your conversation and code the model can consider when generating responses.
What are tokens? Tokens are small chunks of text that AI models read. A token is roughly ¾ of a word. For example, the word “hamburger” becomes two tokens: “ham” and “burger”. When we talk about context window sizes like “128K tokens,” that means the AI can read about 96,000 words at once.
Key point: Larger context windows allow the model to understand more of your codebase at once, but may increase costs and response times.
Quick reference
| Size | Tokens | Approximate Words | Use Case |
|---|
| Small | 8K-32K | 6,000-24,000 | Single files, quick fixes |
| Medium | 128K | ~96,000 | Most coding projects |
| Large | 200K | ~150,000 | Complex codebases |
| Extra Large | 400K+ | ~300,000+ | Entire applications |
| Massive | 1M+ | ~750,000+ | Multi-project analysis |
Model context windows
| Model | Context Window | Effective Window* | Notes |
|---|
| Claude Sonnet 4.5 | 1M tokens | ~500K tokens | Best quality at high context |
| GPT-5 | 400K tokens | ~300K tokens | Three modes affect performance |
| Gemini 2.5 Pro | 1M+ tokens | ~600K tokens | Excellent for documents |
| DeepSeek V3 | 128K tokens | ~100K tokens | Optimal for most tasks |
| Qwen3 Coder | 256K tokens | ~200K tokens | Good balance |
What counts toward context
- Your current conversation - All messages in the chat
- File contents - Any files you’ve shared or CodinIT has read
- Tool outputs - Results from executed commands
- System prompts - CodinIT’s instructions (minimal impact)
Optimization strategies
1. Start fresh for new features
/new - Creates a new task with clean context
- Maximum context available
- No irrelevant history
- Better model focus
2. Use @ mentions strategically
Instead of including entire files:
@filename.ts - Include only when needed- Use search instead of reading large files
- Reference specific functions rather than whole files
3. Enable auto-compact
CodinIT can automatically summarize long conversations to free up space in the context window:
- Settings → Features → Auto-compact
- Preserves important context
- Reduces token usage
Context window warnings
Signs you’re hitting limits
| Warning Sign | What It Means | Solution |
|---|
| ”Context window exceeded” | Hard limit reached | Start new task or enable auto-compact |
| Slower responses | Model struggling with context | Reduce included files |
| Repetitive suggestions | Context fragmentation | Summarize and start fresh |
| Missing recent changes | Context overflow | Use checkpoints to track changes |
Best practices by project size
Small projects (< 50 files)
- Any model works well
- Include relevant files freely
- No special optimization needed
Medium projects (50-500 files)
- Use 128K+ context models
- Include only working set of files
- Clear context between features
Large projects (500+ files)
- Use 200K+ context models
- Focus on specific modules
- Use search instead of reading many files
- Break work into smaller tasks
Advanced context management
Plan/Act mode optimization
Leverage Plan/Act mode for better context usage:
- Plan Mode: Use smaller context for discussion and planning
- Act Mode: Include necessary files when you’re ready to write code
Configuration example:
Plan Mode: DeepSeek V3 (128K) - Lower cost planning
Act Mode: Claude Sonnet (1M) - Maximum context for coding
Context pruning strategies
These are ways CodinIT can reduce the amount of text in your context window:
- Temporal pruning: Remove older parts of your conversation that are no longer relevant
- Semantic pruning: Keep only the code sections related to your current task
- Hierarchical pruning: Keep the big picture but remove fine details
Token counting tips
Rough estimates
- 1 token ≈ 0.75 words (so 1,000 tokens is about 750 words)
- 1 token ≈ 4 characters
- 100 lines of code ≈ 500-1000 tokens
File size guidelines
| File Type | Tokens per KB |
|---|
| Code | ~250-400 |
| JSON | ~300-500 |
| Markdown | ~200-300 |
| Plain text | ~200-250 |
Context window FAQ
Q: Why do responses get worse with very long conversations?
A: Models can lose focus with too much context. The “effective window” is typically 50-70% of the advertised limit.
Q: Should I use the largest context window available?
A: Not always. Larger contexts increase cost and can reduce response quality. Match the context to your task size.
Q: How can I tell how much context I’m using?
A: CodinIT shows token usage in the interface. Watch for the context meter approaching limits.
Q: What happens when I exceed the context limit?
A: CodinIT will either:
- Automatically compact the conversation (if enabled)
- Show an error and suggest starting a new task
- Truncate older messages (with warning)
Recommendations by use case
| Use Case | Recommended Context | Model Suggestion |
|---|
| Quick fixes | 32K-128K | DeepSeek V3 |
| Feature development | 128K-200K | Qwen3 Coder |
| Large refactoring | 400K+ | Claude Sonnet 4.5 |
| Code review | 200K-400K | GPT-5 |
| Documentation | 128K | Any budget model |
