The Final Word on Model Selection

When choosing a model for OpenClaw, it argues you should look at actual success rate rather than gut feel.

Based on PinchBench, you can compare and choose model performance for OpenClaw-style tasks.

• Claude Opus 4.6 is near the top at about 93%
• GPT-5.4 follows closely behind at about 90%
• The Qwen family also stays competitive across sizes

The core message is clear: since agents are about execution, not just conversation, you need to filter out models that talk well but can't get the job done.

Based on this, the author splits up models by role.

• High-success-rate models: important core execution tasks
• Mid-tier models: backup or lightweight tasks
• Local models: cleanup, simple tasks, low-cost execution

Ultimately, the argument is that PinchBench becomes the baseline for model selection, so you should make benchmark-based decisions instead of relying on gut feel.