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)#

ModelContextMultimodalBest ForCostqwen3.5-plus1M✅ Text+ImageGeneral tasks, creative writing, balanced performanceLowqwen3-max262K❌ Text onlyComplex reasoning, deep analysis, researchHighqwen3-coder-plus1M❌ Text onlyCode generation, debuggingMedium

Third-party Models (bailian provider)#

ModelContextMultimodalBest Forglm-51M✅ Text+ImageMultimodal tasks, Chinese optimizationkimi-k2.5200K✅ Text+ImageMultimodal, research-orientedMiniMax-M2.51M✅ Text+ImageLong 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#

1. Primary model selection based on task type
2. If primary model fails, fallback to qwen3.5-plus
3. If still failing, use current session model

Usage Examples#

Automatic Selection#

text

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-max
• Use 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