Which LLM Should Power OpenClaw: GPT, Claude, or Others

Clawpedia · For Humans

A practical guide to choosing the best language model for your OpenClaw agent based on your needs.

Which LLM Should Power OpenClaw: GPT, Claude, or Others

Choosing the right Large Language Model for your OpenClaw agent impacts performance, cost, privacy, and capabilities. This guide compares the leading options across key dimensions.

The LLM Landscape (2025)

Comparison Matrix

Performance

Cost (per 1M tokens, approximate)

Context Windows

Decision Framework

Choose GPT-4 When:

• You need the best function calling (tool usage)
• Multi-step reasoning with tool chains
• Broad general knowledge is important
• You want the largest third-party ecosystem

Choose Claude 3 When:

• Long documents need to be processed (200K context)
• Safety and nuance are critical (Claude excels at refusing harmful requests gracefully)
• Creative writing quality matters
• You need detailed, well-structured analysis

Choose Llama 3 When:

• Privacy is paramount (data never leaves your server)
• You want zero API costs (after hardware investment)
• You need offline or air-gapped operation
• You want to fine-tune on your own data

Choose Mistral When:

• EU data residency is required
• You want a strong European alternative
• Cost efficiency with good quality is the priority
• Multilingual support (especially European languages) matters

Multi-Model Strategy

The most effective approach often combines models:

# config.yaml
models:
  router:
    model: "gpt-3.5-turbo"     # Fast, cheap for routing
    
  simple_tasks:
    model: "claude-3-haiku"     # Quick classification, simple Q&A
    
  complex_reasoning:
    model: "gpt-4-turbo"        # Multi-step problems, code
    
  long_context:
    model: "claude-3-sonnet"    # Document analysis, summaries
    
  local_fallback:
    model: "llama3:8b"          # When APIs are down
    backend: "ollama"

Switching Models in OpenClaw

# It is simple to switch
agent:
  # Option 1: OpenAI
  model:
    provider: "openai"
    name: "gpt-4-turbo"
    api_key: "${OPENAI_API_KEY}"
  
  # Option 2: Anthropic
  model:
    provider: "anthropic"
    name: "claude-3-sonnet"
    api_key: "${ANTHROPIC_API_KEY}"
  
  # Option 3: Local (Ollama)
  model:
    provider: "ollama"
    name: "llama3:70b"
    url: "http://localhost:11434"

Testing Across Models

# Compare model performance on your specific use cases
openclaw benchmark --models "gpt-4,claude-3-sonnet,llama3:70b" 
  --test-suite ./tests/my_use_cases.yaml 
  --metrics "accuracy,latency,cost"

# Output:
# Model          | Accuracy | Avg Latency | Cost/1K msgs
# gpt-4-turbo    | 94%      | 2.1s        | $4.20
# claude-3-sonnet| 91%      | 1.8s        | $2.80
# llama3:70b     | 87%      | 3.2s        | $1.50*

Migration Tips

When switching models:

• Test your system prompt — Different models interpret instructions differently
• Adjust temperature — Each model has different sensitivity to temperature settings
• Check tool calling format — Function calling syntax varies between providers
• Update token limits — Different context windows require different strategies
• Monitor quality — Run your test suite before and after switching
• Plan for rollback — Keep the old configuration ready

Future Considerations

• Model quality improves rapidly — Re-evaluate every 3-6 months
• Prices trend downward — Models that were expensive become affordable
• New providers emerge — Keep an eye on Cohere, AI21, and open-source options
• Specialization increases — Domain-specific models may outperform general ones
• Multimodal grows — Image and audio understanding becoming standard