What's the best AI model for OpenClaw? : OpenClaw Discussion Forums

There's a question we all ask when setting up @OpenClaw: which model should I actually use?

What are your suggestions? Any preferences?

The "best" model definitely depends on your workflows and priorities. High success rate, fast completions, or cost efficient? For coding tasks, there's this thread [1] suggesting @Claude by Anthropic, @Gemini, and @OpenAI's GPT models, while open-weight models like @MiniMax are bridging the gap with every release. [2]

Curious what the community recommends for @OpenClaw?

Note: The poll is inspired by the current leaderboard on pinchbench.com by @KiloClaw

[1]: What's the best AI model for coding?

[2]: MiniMax M2.7 vs. Claude Opus 4.6

Would honestly love to see this split by workflow, because one "best model" overall feels too broad to be useful.

@fmerian Claude Opus 4.6 with extended thinking for everything. Full stop.

We run it across virtually every workflow, coding, security analysis, content, strategy, architecture, all of it. The only exceptions are basic boilerplate and simple admin tasks. The cost premium is worth it because the accuracy and output quality eliminates the rework cycles you'd burn through with cheaper models. You save more in iteration time than you spend on tokens.

We're consistently hitting 95%+ accuracy across tasks, but here's the part nobody talks about: that's not just the model. We've built an extensive custom context memory and state management system that feeds the model exactly what it needs across sessions. Persistent memory, governance documents, project state, lessons learned, all structured and available in every interaction.

This is the biggest gap in how most people use these models. Even Opus 4.6 in extended thinking will underperform if you're starting every conversation from zero with no context, no memory, and no structured state. The model is the engine but the context system is the fuel. Without it, every model, no matter how powerful, is reasoning in a vacuum. The teams investing in memory architecture and context management are getting dramatically better results than teams just picking the "best model" and hoping for the best.

The model matters. How you feed it matters more.

This is super useful, especially seeing success rates across models in one place. One thing I’ve noticed while working with OpenClaw is that people don’t just struggle with “which model performs best” , they struggle with “what setup should I actually run without burning through budget”. Curious if you’ve thought about combining benchmark data with real-world cost estimation? Would make the decision much clearer for most users.

What's the best AI model for OpenClaw?

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