Spent a Week Finding the Most Cost-Effective and Powerful OpenClaw Model Config (Claude+Kimi+DeepSeek)

Comparison of OpenClaw Model Configurations (The Golden Triangle: Claude + Kimi + DeepSeek)

I did something stupid last week.

I set OpenClaw's default model to Claude 4.5, and then let it refactor an old Python project.

The result was, I woke up the next morning, the project didn't run, and the OpenRouter bill exploded first. That number made my heart ache.

This made me realize a problem: Are we thinking of AI Agents as too 'monolithic'?

If you are in a company with an unlimited budget, of course, you can be served by GPT-5.3 Codex or Claude 4.5 all the way. But for independent developers, this is not realistic.

And, it's not necessary.

After repeated debugging, I finally figured out a "Golden Triangle" configuration. This scheme reduced my Token consumption by 90%, and the work efficiency is even higher than using Claude alone.

The Misconception: One Model to Rule Them All

Many people (including me before) configure OpenClaw with only one line: "model": "anthropic/claude-4-5-sonnet"

This is like using a Ferrari to deliver food delivery.

Claude 4.5 is indeed smart, it is the current SOTA. But do you really need a million-dollar architect to write console.log for you or fix a stupid Bug like missing semicolon?

No, you don't.

Agent work is actually layered. Some work requires IQ, some work requires memory, and most work just needs to be durable.

The Golden Triangle Architecture

I split my OpenClaw into three roles:

1. 🧠 The Architect: Claude 4.5

• Positioning: Expensive, scarce, decision maker.
• Responsibilities: Top-level design, complex logic judgment, formulating SOP.
• When to call: When you ask "How should this feature be designed?" or "Help me review the security vulnerabilities of this authentication logic".
• Detailed config: Set it as planner or reviewer.

2. 📚 The Librarian: Kimi 2.5

• Positioning: Super large memory, never forgets.
• Responsibilities: Read massive documents, analyze the entire codebase structure, read hundreds of pages of API manuals.
• Why Kimi?: In the field of Long Context, Kimi is still the king. Throw dozens of files at it, and it can accurately tell you which variable was defined in which file.
• When to call: When you start a task, let it scan the src/ directory first; or let it summarize yesterday's execution.log.

3. 👷 The Intern: DeepSeek R1

• Positioning: Cheap, obedient, durable.
• Responsibilities: Write specific functions, fix simple Bugs, generate JSON data, run test scripts.
• Why DeepSeek?: Because it's cheap. Extremely cheap. Although its Coding ability is not as amazing as Claude 4.5, it is completely sufficient for writing specific business logic. Most importantly, Agents often fall into a "trial and error loop". At this time, it doesn't hurt to use DeepSeek to run ten loops.
• When to call: fix_error, generate_code, run_script.

Show Me The Code

In OpenClaw's config.json, I configured the Router like this:

{
  "router": {
    "defaults": {
      "model": "deepseek/deepseek-r1"
    },
    "overrides": [
      {
        "taskType": ["planning", "reasoning", "security_audit"],
        "model": "anthropic/claude-4-5-sonnet"
      },
      {
        "taskType": ["context_reading", "summary", "search"],
        "model": "moonshot/kimi-2.5-128k"
      },
      {
        "taskType": ["coding", "debugging", "scripting"],
        "model": "deepseek/deepseek-r1"
      }
    ]
  }
}

(Note: The above is pseudo-code, specific configuration depends on your OpenClaw version and plugin support)

How is the effect?

Since switching to this configuration:

1. Save Money: Daily development costs dropped from $10 to about $1. DeepSeek assumed 80% of Token consumption, but only accounted for 10% of the cost.
2. No Confusion: Before, the context was too long, and Claude would also get dizzy. Now, verify the "reading comprehension" task to Kimi, it summarizes the abstract and feeds it to other models, the context is extremely refreshing.
3. Dare to Trial and Error: Before, seeing an error report, I didn't dare to let the Agent fix it automatically, afraid of wasting money. Now I throw it directly to DeepSeek: "Go fix it, don't come back if you can't fix it", no psychological burden.

Summary

Don't be superstitious about the "strongest model". In the world of Agents, there is no strongest, only the most suitable.

• Claude is your CTO.
• Kimi is your library.
• DeepSeek is your super intern who works tirelessly and sleeps in a sleeping bag at the workstation.

Combine them, and only then do you have a real team.

P.S. I wrote a script specifically to monitor the API status of these three models and the release progress of DeepSeek V4. Those interested can come and see: DeepSeekV4.app