> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cline.bot/llms.txt
> Use this file to discover all available pages before exploring further.

# Context Window Guide

> Understanding and managing AI model context windows

## 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.

<Note>
  **Key Point**: Larger context windows allow the model to understand more of your codebase at once, but may increase costs and response times.
</Note>

## Context Window Sizes

### 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                   |

\*Effective window is where model maintains high quality

## Managing Context Efficiently

### What Counts Toward Context

1. **Your current conversation** - All messages in the chat
2. **File contents** - Any files you've shared or Cline has read
3. **Tool outputs** - Results from executed commands
4. **System prompts** - Cline's instructions (minimal impact)

### Optimization Strategies

#### 1. Start Fresh for New Features

```text theme={"system"}
/new - Creates a new task with clean context
```

Benefits:

* 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

Cline can automatically summarize long conversations:

* 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
* **Act Mode**: Include necessary files for implementation

Configuration:

```text theme={"system"}
Plan Mode: DeepSeek V3 (128K) - Lower cost planning
Act Mode: Claude Sonnet (1M) - Maximum context for coding
```

### Context Pruning Strategies

1. **Temporal Pruning**: Remove old conversation parts
2. **Semantic Pruning**: Keep only relevant code sections
3. **Hierarchical Pruning**: Maintain high-level structure, prune details

### Token Counting Tips

#### Rough Estimates

* **1 token ≈ 0.75 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:** Cline shows token usage in the interface. Watch for the context meter approaching limits.

### Q: What happens when I exceed the context limit?

**A:** Cline 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  |