There is no universal best model. The right default depends on the job, but a hosted model with strong instruction following is usually the safest starting point before you optimize for cost.
Models & Cost
Pick the right model before you burn time or budget
Model choice is an architecture decision, not just a pricing decision. Balance quality, latency, cost, and privacy based on the workflows your OpenClaw agent actually runs.
Claude Sonnet / Opus
Best overall quality, support, and instruction following.
Higher cost than local models and some hosted alternatives.
GPT-4o / GPT-4
Fast coding-heavy workloads and wide ecosystem support.
Can get expensive when long contexts or large volumes are involved.
Gemini
Lower-friction experimentation and generous entry pricing.
Behavior can differ across features, so test carefully before production.
Ollama / LM Studio
Privacy-sensitive workloads and predictable marginal cost.
You own the hardware, latency, and model-quality tradeoffs.
Model cost per use case
Match the model to the workflow. Not every task needs the most capable or most expensive model.
Latency and throughput
Measure time to first token and peak concurrency before committing to a production provider.
Privacy and control
If data residency or private context matters, local or self-hosted models are usually worth the tradeoff.
Run the numbers before you commit
Use the calculator to estimate hosted spend, then decide whether local models or routing logic are worth it.
Open the cost calculatorSources referenced on this page
These references keep the model and provider discussion tied to the primary docs rather than generic provider summaries.
OpenClaw models FAQ
These answers are designed for high-intent research queries around OpenClaw model selection, cost, and local-versus-hosted tradeoffs.
Use local models when privacy, predictable marginal cost, or offline operation matter more than absolute quality and minimal setup complexity.
Match model quality to workflow, route expensive tasks narrowly, measure token usage, and validate whether local or cheaper fallback models can handle lower-risk work.