OpenClaw is expensive — why the agent burns through tokens and how to save

OpenClaw is an autonomous agent with a multi-layered architecture. Unlike simple assistants, where one prompt goes to the model and returns a response, OpenClaw builds a chain of several roles and several iterations. Each link in the chain consumes tokens, and the total consumption for a single user task exceeds that of a chat assistant by an order of magnitude or more.

Typical OpenClaw workflow for the task "write module X":

1. Planner reads the task description and the entire project context (~30K input + 2K output)
2. Decomposer breaks the plan into sub-tasks (~20K input + 1K output)
3. Executor for each sub-task: reads files, generates code, applies patches (5-15 iterations × ~50K input + 3K output)
4. Critic checks results and suggests corrections (~40K input + 2K output)
5. Corrector applies fixes (5-10 iterations × ~30K input + 2K output)
6. Final verification and report generation (~30K input + 1.5K output)

Add it all up — for an average task, OpenClaw consumes 800K-1.5M input tokens and 50-120K output tokens. For complex tasks with long autonomous iterations, consumption grows to 5-15M input + 200-500K output.

Real figures for specific types of tasks:

• Simple feature (one function with a test): ~600K total tokens ≈ $3 on Anthropic
• Medium feature (new module of 200 lines): ~3M total tokens ≈ $12
• Complex feature (refactoring + new functionality): ~10M total tokens ≈ $35
• Long autonomous task (hour-long run with critic and iterations): 30-50M total tokens ≈ $100-170
• Full agent day with several tasks in OpenClaw: 100-200M total tokens ≈ $350-700

The main difference from Cline or Cursor is that OpenClaw makes 3-5 role calls at each step, whereas Cline makes one. This is not a bug — it's a feature that improves decision-making quality and reduces errors. But financially, it also makes OpenClaw the most expensive agentic tool on the market when using Anthropic or OpenAI directly.

Comparison of consumption speed with other tools for the same task:

• Cursor Agent: ~5K-50K tokens per task
• Cline: ~500K-5M tokens per task
• Claude Code: ~200K-3M tokens per task
• OpenClaw: ~3M-50M tokens per task (×5-10 of Cline)

OpenClaw supports any OpenAI-compatible provider via environment variables and a config file. This means that switching from Anthropic API to JoinGonka Gateway requires zero changes in the OpenClaw code itself — only changing the endpoint and API key.

Comparison by task type:

The multi-level architecture of OpenClaw, which makes it expensive on Anthropic, turns into an advantage on JoinGonka: more role calls = more decision-making accuracy, and now it costs almost nothing. You can enable all critics and checkers, leave autonomous runs overnight, and experiment with long chains — without fearing a four-digit bill in the morning.

JoinGonka Gateway charges for input and output — fractions of a cent per million tokens (output is more expensive than input). At Anthropic, input is $3, output is $15: even JoinGonka's output is hundreds of times cheaper, which is especially beneficial for OpenClaw, generating many output tokens during role-based exchanges.

Under the hood is the Kimi K2.6 model (MoE architecture). For role-based tasks (planning, execution, criticism), its capabilities for structured output and tool calling are significant: the model supports native tool calling. On the SWE-bench benchmark, which measures autonomous development quality, Kimi K2.6 is on par with Claude Sonnet 4.6. More details are in the article about Qwen3-235B. General market context is in the overview of the cheapest AI API in 2026.

The easiest way is using the one-command installer: it will automatically add the JoinGonka provider to ~/.openclaw/openclaw.json with the correct baseUrl and models, while backing up your current config:

npx @joingonka/setup --tool openclaw

This is the universal JoinGonka installer—without the npx @joingonka/setup flag, it will prompt you to select a tool (Claude Code, OpenClaw, or Cline), ask for your API key (jg-…), and add only the JoinGonka provider without touching your other settings. Below is the manual way if you prefer to configure it yourself.

{
  "models": {
    "providers": {
      "gonka": {
        "baseUrl": "https://gate.joingonka.ai/v1",
        "api": "openai-completions",
        "apiKey": "${GONKA_API_KEY}",
        "models": [
          { "id": "moonshotai/Kimi-K2.6", "name": "Kimi K2.6", "maxTokens": 8192 },
          { "id": "MiniMaxAI/MiniMax-M2.7", "name": "MiniMax M2.7", "maxTokens": 8192 }
        ]
      }
    }
  },
  "agents": {
    "defaults": {
      "model": { "primary": "gonka/moonshotai/Kimi-K2.6" }
    }
  }
}

export GONKA_API_KEY=jg-your-key

Let's compare three typical OpenClaw production usage profiles.

Profile 1: "Agent experiment". A developer runs OpenClaw 5—10 times a week, mostly on medium tasks to evaluate quality. Monthly consumption — ~50M total tokens.

• Anthropic: 50M × $0.005 ≈ $250/mo
• JoinGonka: 50M × $0.0048 = $0.24/mo. Savings — 1040x.

Profile 2: "Regular use as part of workflow". OpenClaw runs on complex tasks daily, sometimes left for long autonomous sessions. Monthly consumption — ~500M total tokens.

• Anthropic: 500M × $0.005 ≈ $2500/mo
• JoinGonka: 500M × $0.0048 = $2.40/mo. Savings — 1040x.

Profile 3: "Production-pipeline on OpenClaw". The team has automated parts of their workflows through OpenClaw — report generation, refactoring old code, code review. Consumption — ~3B total tokens per month.

• Anthropic: 3B × $0.005 = $15000/mo
• JoinGonka: 3B × $0.0048 = $14.40/mo. Savings — 1040x.

At the Profile 3 level, the effect is particularly interesting — OpenClaw turns from "too expensive for regular automation" to "so cheap that you can automate everything possible." This changes the very economics of decision-making: a task that previously seemed too expensive for an agent can now be offloaded to it without a second thought.

On an annual horizon, an active user saves about $30,000, a team — $180,000. This is not just budget optimization, but a qualitative change in how a team uses agentic AI: free instead of "on budget."

At the same time, the OpenClaw tool itself remains unchanged: the same role-based pipelines, the same quality decomposition, the same control via critics. Only the source of inference changes — and with it, the economics of the entire workflow.

Strategy for mixing models in OpenClaw. OpenClaw supports different models for different roles in a pipeline. Through JoinGonka Gateway, you can assign MiniMax M2.7 for all stages (general-purpose model), or combine it with Kimi K2.6 for critics and final verification — Kimi has a long context and strong reasoning, which is especially useful when evaluating multi-step results. Since both models are priced at $0.003/1M, there is no financial bonus from using a "lighter" model in cheap roles — but you can fine-tune output quality for each stage of the pipeline.

Production case: automated code review. One of the real scenarios made possible by JoinGonka economics is automatic code review for every pull request via OpenClaw. Pipeline: "read diff → analyze each file → check test coverage → compile final report". On Anthropic, this pipeline would consume ~$5—15 per PR; on JoinGonka — $0.01—0.024. A team of 10 developers making 50 PRs a day goes from $750/day on Anthropic to $1.20/day on JoinGonka — and the code review agent turns from a luxury into a daily workflow.