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

1. 1. Primary model selection based on task type
2. If primary model fails, fallback to INLINECODE7
3. If still failing, use current session model

Usage Examples

Automatic Selection

CODEBLOCK0

Manual Override

Users can always specify models directly:

• - INLINECODE8
• INLINECODE9

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 INLINECODE10
• Version updates maintain backward compatibility
• New task types can be added without breaking existing functionality