Intelligent Model Routing: RouteLLM vs LiteLLM for Cost-Effective OpenClaw Agents

One of the most common questions in the OpenClaw Discord: "How do I avoid burning money on Opus for simple tasks?"

The answer isn't just picking a cheaper model—it's picking the right model for each task. Today we're looking at two community-tested approaches: RouteLLM and LiteLLM.

The Problem

Running everything through Claude Opus 4.6 (or similar frontier models) is expensive. As @Jerry shared in Discord:

"I just got routeLLM working and it'll assign the task to sonnet or opus depending on how difficult it is."

But @Casimir1904 had a different experience with automatic routing:

"openrouter auto did suck for me.. Lot routed via gpt nano and that was BS lol"

So what's the solution?

Option 1: RouteLLM (Two-Model Routing)

RouteLLM is purpose-built for binary model selection. It uses a trained classifier to decide:

• Simple task? → Send to the cheaper model (e.g., Sonnet 4.6)
• Complex task? → Send to the smarter model (e.g., Opus 4.6)

Pros:

• Fast, lightweight classification
• Works well for clear simple/complex splits
• Community members report good results for coding tasks

Cons:

• Limited to two models
• You need to train or tune the router for your use case
• May not handle edge cases well

Setup tip: RouteLLM works best when your two models have clearly different strengths. Sonnet + Opus is a natural pairing.

Option 2: LiteLLM (Multi-Model Proxy)

LiteLLM takes a different approach—it's a unified proxy that can route to any model with custom logic.

As @Ineffigy mentioned:

"I don't know if it is better, but I use LiteLLM for that."

Pros:

• Route to multiple models (not just two)
• Custom routing logic (cost, latency, token limits)
• Acts as a drop-in OpenAI-compatible API
• Great for fallbacks and load balancing

Cons:

• More setup complexity
• You define the routing rules yourself
• Requires running another service

The Native OpenClaw Alternative

Before reaching for external tools, remember OpenClaw has built-in multi-agent support. @Casimir1904's approach:

"Try to setup agents/sub agents and your main agent just assigning tasks to them. Imo the biggest token wasting is from tool calls in the main agent/session..."

This means:

1. Main agent uses a smart model (Opus) for orchestration
2. Sub-agents use cheaper models for specific tasks
3. Configure models per-agent in openclaw.json

Example config snippet:

{
  "agents": [
    {
      "id": "main",
      "model": "anthropic/claude-opus-4-6"
    },
    {
      "id": "deploy-ops",
      "model": "anthropic/claude-sonnet-4-6"
    },
    {
      "id": "docs-writer",
      "model": "openrouter/glm-5-pro"
    }
  ]
}

Which Should You Choose?

Community Tip

Don't forget @Casimir1904's advice:

"Also try to use more real cronjobs for stuff that doesn't need AI.. I do lot via pm2 = 0 tokens once setup..."

Sometimes the best routing is routing tasks away from AI entirely.

Have you tried RouteLLM or LiteLLM with OpenClaw? Share your setup in the comments!

Source: OpenClaw Discord #general discussion, February 18, 2026