OpenClaw Best Practices - Multi-Model Switching

Overview

Supported Model Ecosystem

Cloud Models

Local Models (Ollama)

Multi-Model Strategies

Strategy 1: Task Routing

Automatically select the most suitable model based on task type:

Strategy 2: Fallback Chain

Automatically switch when primary model is unavailable:

Claude 4 → GPT-4o → DeepSeek → Local Llama

Strategy 3: Cost Optimization

• Try local models first, upgrade to cloud if insufficient
• Use low-cost models for simple tasks
• Set daily/monthly API budget limits
• Cache frequent queries to reduce API calls

Strategy 4: Privacy Grading

Configuration Example

models:
  default: claude-4

  routing:
    simple_chat: ollama/llama3.2:3b
    complex_reasoning: claude-4
    image_understanding: gpt-4o
    chinese_text: deepseek-v3
    code_generation: claude-4
    privacy_sensitive: ollama/llama3.2:7b

  fallback:
    - claude-4
    - gpt-4o
    - deepseek-v3
    - ollama/llama3.2:7b

  budget:
    daily_limit: 2.00  # USD
    monthly_limit: 50.00
    alert_threshold: 0.8  # Alert at 80%

Model Selection Decision Tree

Task Arrives
  ├── Privacy-Sensitive? → Local Model
  ├── Needs Image Understanding? → GPT-4o
  ├── Complex Reasoning? → Claude 4
  ├── Chinese Content? → DeepSeek/Qwen
  ├── Simple Q&A? → Local Small Model
  └── Default → Configured Default Model

Cost Monitoring

Key Metrics

Optimization Suggestions

1. Increase local model usage ratio (target >50%)
2. Optimize system prompt length
3. Use streaming output to reduce perceived latency
4. Enable caching for similar queries

Summary

The core of multi-model switching is "the right model for the right task." By combining task routing, fallback chains, cost optimization, and privacy grading, the best balance of performance, cost, and privacy can be achieved.

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*Analysis Date: March 28, 2026*