Auto Model Switcher
Intelligently selects the optimal model from available providers based on task characteristics.
When to Use
- Task requires specific capabilities (coding, analysis, multimodal, writing, research)
- Need optimal performance/cost balance
- Working with long context or complex reasoning
- User doesn't specify a model preference
Available Models Analysis
Qwen Series (bailian provider)
| Model | Context | Multimodal | Best For | Cost |
|---|---|---|---|---|
| qwen3.5-plus | 1M | ✅ Text+Image | General tasks, creative writing, balanced performance | Low |
| qwen3-max | 262K | ❌ Text only | Complex reasoning, deep analysis, research | High |
| qwen3-coder-plus | 1M | ❌ Text only | Code generation, debugging | Medium |
Third-party Models (bailian provider)
| Model | Context | Multimodal | Best For |
|---|---|---|---|
| glm-5 | 1M | ✅ Text+Image | Multimodal tasks, Chinese optimization |
| kimi-k2.5 | 200K | ✅ Text+Image | Multimodal, research-oriented |
| MiniMax-M2.5 | 1M | ✅ Text+Image | Long context multimodal |
Selection Logic
Task Type Detection
Code Tasks → bailian/qwen3-coder-plus
- Keywords: code, programming, debug, fix, implement, develop, coding, script
- File extensions: .py, .js, .ts, .java, .cpp, etc.
- Commands: git, npm, docker, build, compile
Complex Analysis → bailian/qwen3-max
- Keywords: analyze, research, compare, evaluate, strategy, deep dive, business analysis
- Tasks requiring multi-step reasoning
- Financial/strategic analysis
Research Tasks → bailian/qwen3-max
- Keywords: research, investigate, study, survey, academic, literature review
- Complex information synthesis
- Multi-source analysis and comparison
Writing/Copywriting Tasks → bailian/qwen3.5-plus
- Keywords: write, draft, copywriting, content, article, blog, email, proposal, creative
- Marketing copy, social media content
- Creative writing and storytelling
Multimodal Tasks → bailian/glm-5
- Image analysis, OCR, visual understanding
- Audio processing (when supported)
- Mixed text+image inputs
Long Context → bailian/qwen3.5-plus
- Document processing > 200K tokens
- Summarization of large documents
- Historical context analysis
General Tasks → bailian/qwen3.5-plus (default)
- Chat, simple queries, basic tasks
- When no specific requirements detected
Fallback Strategy
- Primary model selection based on task type
- If primary model fails, fallback to
qwen3.5-plus - If still failing, use current session model
Usage Examples
Automatic Selection
User: Help me debug this Python code
→ Model: bailian/qwen3-coder-plus
User: Analyze our Q4 financial performance vs competitors
→ Model: bailian/qwen3-max
User: Research the latest AI trends in marketing
→ Model: bailian/qwen3-max
User: Write a compelling product description for our new service
→ Model: bailian/qwen3.5-plus
User: What's in this image?
→ Model: bailian/glm-5
User: Summarize this 500-page document
→ Model: bailian/qwen3.5-plus
Manual Override
Users can always specify models directly:
/model bailian/qwen3-maxUse coder model for this task
Implementation Notes
- Always check if target model is available before switching
- Preserve current session context when switching
- Log model selections for learning and optimization
- Respect user's explicit model preferences
Security Considerations
- Only switch between pre-configured models in openclaw.json
- Never attempt to use unconfigured or unknown models
- Validate model names against available list before switching
Performance Metrics
Track these metrics for continuous improvement:
- Task completion success rate by model
- Response time by model and task type
- User satisfaction feedback
- Cost per task type
This skill enables intelligent model routing without user intervention while maintaining full control when needed.
Iteration Support
- Skills can be updated via
clawhub sync --all - Version updates maintain backward compatibility
- New task types can be added without breaking existing functionality