OpenClaw works with dozens of LLM providers — Claude, GPT-4, Gemini, Llama, Mistral, and more. Each model has different strengths: some excel at coding, others at analysis, some are blazing fast, others are dirt cheap. Choosing the wrong model means you either overpay for capabilities you don't need, or under-deliver on quality for tasks that demand precision. This guide helps you match models to your actual needs.
Why This Is Hard to Do Yourself
These are the common pitfalls that trip people up.
Too many model options
Anthropic has 4+ Claude models, OpenAI has 6+ GPT variants, Google has Gemini Pro/Flash/Ultra, plus dozens of open-source options. How do you choose?
Cost vs quality tradeoff
The best model (Claude Opus, GPT-4) costs 10-20x more than budget models (Haiku, GPT-3.5). Is the quality difference worth it for your use case?
Different models for different tasks
No single model is best for everything. You need fast cheap models for simple queries, powerful expensive models for complex reasoning, and specialized models for code or analysis.
Step-by-Step Guide
Understand what models OpenClaw supports
OpenClaw integrates with all major LLM providers.
Warning: Start with one provider (Anthropic or OpenAI) to keep things simple, then add others as you identify specific needs for cheaper or specialized models.
Compare model capabilities
Know what each model is actually good at.
Warning: Don't default to the most expensive model for everything. Most queries don't need Opus-level reasoning and work fine on Sonnet or Haiku.
Compare pricing tiers
Know what you're paying per query.
Warning: For most production use cases, Claude Sonnet offers the best quality-to-cost ratio. Reserve Opus for the 10% of queries that truly need it.
Match models to use cases
Route different tasks to different models.
Warning: Over-routing to expensive models wastes money. Under-routing to cheap models frustrates users with poor quality. Start conservative (use Sonnet for most things) and optimize over time.
Configure model routing
Set up automatic model selection.
Warning: Monitor which routes are actually being used. If "simple" route is rarely triggered, your triggers are too conservative — loosen them to save money.
Monitor and adjust
Track quality vs cost and optimize.
Warning: Don't optimize purely for cost — users will notice quality degradation. Aim for lowest cost that maintains acceptable quality for each use case.
Choosing Between Models?
Generic benchmarks don't reflect your actual workload. Our experts benchmark Claude, GPT-4, Gemini, and open-source models against your real queries, measure quality-vs-cost tradeoffs for your specific use cases, and recommend the optimal model mix. We'll configure routing rules and monitor performance to ensure you're getting the best value.
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