Building a Cost-Effective Multi-Model Agent Lineup in OpenClaw

One of the most powerful features of OpenClaw is the ability to mix and match models from different providers for specialized tasks. Community member Lucky777Monkey recently shared their "cost-effective lineup" in Discord, sparking a great discussion about role-based agent architecture.

The Concept: Specialized Workers

Instead of running one expensive model for everything, you can assign different models to different agent roles:

Why This Architecture Works

The orchestrator handles routing and high-level decisions — it doesn't need to be the smartest model, just quick and reliable. GPT-5-mini or similar models work well here.

Code specialists can be cheaper models that excel at code. DeepSeek and Kimi are both strong here. Community feedback: consider swapping Kimi to coding and DeepSeek to research — Kimi K2.5 has excellent code generation at a good price.

Local reasoning with Ollama means zero API costs for tasks that need deep thinking but don't require internet access. Running qwen3:32b on a Mac Studio (or similar hardware) gives you a free reasoning workhorse.

Premium escalation — reserve Claude Opus or similar for genuinely hard problems. The "last resort" framing helps your orchestrator understand this is expensive.

Configuration Example

In your clawd.yaml:

agents:
  main:
    model: openrouter/openai/gpt-5-mini
    role: orchestrator
    
  worker-code:
    model: openrouter/deepseek/deepseek-coder-v3.2
    role: code specialist
    
  worker-research:
    model: openrouter/moonshot/kimi-k2.5
    role: research specialist
    
  worker-reason:
    model: ollama/qwen3:32b
    role: deep reasoning (local)
    
  worker-deep:
    model: anthropic/claude-opus-4-6
    role: premium escalation

Community Tips

• Don't lock into a provider: Use OpenRouter or direct APIs so you can swap models easily as pricing changes
• Consider Kimi's Coding Plan: If you're generating lots of code, their dedicated plan can be very cost-effective
• Local Ollama for reasoning: Models like qwen3:32b or mixtral can handle most reasoning tasks without API calls
• Test your routing: Make sure your orchestrator actually routes to workers instead of trying to do everything itself

What About MiniMax?

Several community members have moved away from MiniMax after finding the quality drop from Opus/Sonnet too noticeable. If you're on a budget, mixing a cheap orchestrator with premium escalation often feels better than running a mid-tier model for everything.

Based on a Discord discussion from Feb 18, 2026. Thanks to Lucky777Monkey and reddev for sharing their setups!