Context Window Guide
Understanding and managing AI model context windows
Understanding Context Windows
The context window represents the total volume of text an AI model can analyze simultaneously. Consider it the model's "short-term memory" - determining how much conversation history and code the model can reference while crafting its responses.
Essential Insight: While larger context windows enable models to process more of your project simultaneously, they can also lead to higher expenses and longer processing times.
Available Context Window Capacities
Quick Overview
| Size | Tokens | Approximate Words | Use Case |
|---|---|---|---|
| Small | 8K-32K | 6,000-24,000 | Individual files, minor corrections |
| Medium | 128K | ~96,000 | Standard development projects |
| Large | 200K | ~150,000 | Intricate code repositories |
| Extra Large | 400K+ | ~300,000+ | Complete application systems |
| Massive | 1M+ | ~750,000+ | Cross-project examination |
Context Capacity by Model
| Model | Context Window | Effective Window* | Notes |
|---|---|---|---|
| Claude Sonnet 4.5 | 1M tokens | ~500K tokens | Maintains excellence with extensive context |
| GPT-5 | 400K tokens | ~300K tokens | Performance varies across three operational modes |
| Gemini 2.5 Pro | 1M+ tokens | ~600K tokens | Outstanding for document-heavy tasks |
| DeepSeek V3 | 128K tokens | ~100K tokens | Ideal range for typical workflows |
| Qwen3 Coder | 256K tokens | ~200K tokens | Well-proportioned capacity |
*Effective window represents the range where models deliver peak quality
Efficient Context Management
Elements That Consume Context
- Your current conversation - Every message within the session
- File contents - Documents you've provided or Sypha has accessed
- Tool outputs - Command execution results
- System prompts - Sypha's operational instructions (negligible footprint)
Optimization Techniques
1. Begin Fresh for Distinct Features
/new - Initiates a new task with pristine contextAdvantages:
- Full context capacity available
- Eliminates unrelated conversation history
- Improves model concentration
2. Apply @ Mentions Thoughtfully
Rather than loading complete files:
@filename.ts- Add only when essential- Prefer search functionality over reading large documents
- Target specific functions instead of entire files
3. Activate Auto-compact
Sypha offers automatic conversation condensation:
- Settings → Features → Auto-compact
- Maintains critical context
- Minimizes token consumption
Context Capacity Alerts
Indicators of Approaching Limits
| Warning Sign | What It Means | Solution |
|---|---|---|
| "Context window exceeded" | Maximum capacity reached | Begin new task or activate auto-compact |
| Slower responses | Model processing difficulties | Decrease included file count |
| Repetitive suggestions | Context fragmentation occurring | Condense conversation and restart |
| Missing recent changes | Context capacity overrun | Apply checkpoints to monitor modifications |
Recommended Practices by Repository Size
Compact Projects (< 50 files)
- Any model performs adequately
- Add relevant files without restriction
- Standard optimization unnecessary
Mid-Size Projects (50-500 files)
- Select models with 128K+ context capacity
- Add only actively-used file sets
- Reset context between feature implementations
Extensive Projects (500+ files)
- Choose models offering 200K+ context capacity
- Concentrate on particular modules
- Utilize search rather than reading numerous files
- Divide work into manageable segments
Advanced Context Techniques
Plan/Act Mode Context Efficiency
Take advantage of Plan/Act mode for smarter context utilization:
- Plan Mode: Apply smaller context for strategy discussions
- Act Mode: Load required files for actual implementation
Configuration:
Plan Mode: DeepSeek V3 (128K) - Economical planning phase
Act Mode: Claude Sonnet (1M) - Full context for development phaseContext Reduction Approaches
- Temporal Pruning: Eliminate outdated conversation segments
- Semantic Pruning: Retain only pertinent code sections
- Hierarchical Pruning: Preserve high-level architecture, trim granular details
Token Estimation Guidelines
Approximate Calculations
- 1 token ≈ 0.75 words
- 1 token ≈ 4 characters
- 100 lines of code ≈ 500-1000 tokens
File Size Reference
| File Type | Tokens per KB |
|---|---|
| Code | ~250-400 |
| JSON | ~300-500 |
| Markdown | ~200-300 |
| Plain text | ~200-250 |
Context Window Frequently Asked Questions
Q: Why does response quality decline in very lengthy conversations?
A: Models may lose concentration when processing excessive context. The "effective window" typically spans 50-70% of the maximum advertised capacity.
Q: Is it beneficial to always choose the biggest context window?
A: Not necessarily. Expanded contexts raise expenses and may diminish response quality. Select context capacity appropriate to your task requirements.
Q: How do I monitor my current context consumption?
A: Sypha displays token usage within the interface. Monitor the context indicator as it nears capacity limits.
Q: What occurs when I surpass the context capacity?
A: Sypha will either:
- Automatically condense the conversation (when enabled)
- Display an error suggesting task restart
- Remove earlier messages (accompanied by a warning)
Guidance by Specific 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 |