What is model splitting in OpenClaw?

Model splitting means using expensive models (like Claude Opus) only for complex tasks that need deep reasoning, while routing routine tasks to cheaper models (Sonnet, Haiku, or local models). A main agent on Opus orchestrates subagents on Sonnet — getting 90% of the quality at 30% of the cost.

Can I use local models with OpenClaw to reduce costs?

Yes. OpenClaw supports local models through Ollama and other providers. Use local models for high-volume, lower-complexity tasks like data extraction, classification, and formatting. Combined with cloud models for reasoning, this can cut costs dramatically.

What's the cheapest way to run a useful OpenClaw agent?

Minimum viable setup: Claude Sonnet for main agent ($50-100/month API), existing hardware (no hosting cost), 3-5 cron jobs. Total: ~$100/month. For even cheaper operation, use Haiku or local models for routine tasks and reserve Sonnet for important work.

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OpenClaw Cost Optimization

Q: How much does it cost to run an OpenClaw agent?

Typical costs range from $100-750/month depending on usage. Light personal use: $100-200/month. Heavy business automation: $300-750/month. With optimization (model splitting, caching, local models), you can reduce costs by 40-60%.

Running AI agents doesn't have to break the bank. Model splitting, token management, and smart scheduling can cut your costs by 60%+ without sacrificing quality.

Save 40-60%Model SplittingLocal Models

Understanding AI Agent Costs

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Light Use

$100-200/mo

• Personal assistant
• 3-5 cron jobs
• Sonnet/Haiku models
• Existing hardware

MOST COMMON

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Medium Use

$300-500/mo

• Business automation
• 10-15 cron jobs
• Opus + Sonnet splitting
• Cloud or dedicated hardware

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Heavy Use

$500-1500/mo

• Multi-agent systems
• 20+ cron jobs
• Opus-heavy workloads
• Content generation at scale

Model Splitting Strategy

The #1 cost optimization technique: use the right model for the right task. Not every task needs the most expensive model. Strategic model splitting can cut costs by 40-60% with minimal quality loss.

The Model Hierarchy

Claude Opus 4 — The Strategist

~$15/MTok input

Complex reasoning, architecture decisions, creative writing, orchestration. Use for main agent sessions and tasks requiring deep thought.

Claude Sonnet 4 — The Workhorse

~$3/MTok input

Code generation, content writing, data analysis, most subagent tasks. Best balance of quality and cost for 80% of tasks.

Claude Haiku 3.5 — The Sprinter

~$0.25/MTok input

Quick lookups, classification, formatting, simple summaries. 60x cheaper than Opus — perfect for high-volume, simple tasks.

Local Models (Qwen, Llama) — Free

$0/MTok

Data extraction, pattern matching, boilerplate generation. Runs on your hardware with zero API cost via Ollama.

Practical Model Assignment

TaskModelEst. Cost

Main agent conversationsOpus$3-5/day

Code generation subagentsSonnet$1-2/task

Content writingSonnet + thinking$0.50-2/article

Morning briefing cronSonnet$0.15/day

Heartbeat checksHaiku$0.02/check

Data extractionLocal (Qwen)$0/run

Health monitoringSonnet$0.10/check

Memory maintenanceHaiku$0.05/run

Token Management

Reduce Input Tokens

• Keep HEARTBEAT.md under 200 words
• Use concise SKILL.md instructions
• Load only relevant memory files
• Limit MEMORY.md to ~2,000 words
• Use QMD search instead of reading all files

Reduce Output Tokens

• Tell agents to be concise in cron outputs
• Use structured formats over prose
• Set reasonable token limits per task
• Avoid verbose logging in automated jobs
• Use thinking mode only when needed

Local Models: Zero-Cost Processing

Running Local Models with Ollama

For high-volume, lower-complexity tasks, local models eliminate API costs entirely.Ollama makes it easy to run models like Qwen, Llama, and Mistral on your own hardware.

# Install and run Ollama
curl -fsSL https://ollama.ai/install.sh | sh
ollama pull qwen2.5:32b

# Configure in OpenClaw for specific tasks
model_overrides:
  data_extraction: "ollama/qwen2.5:32b"
  classification: "ollama/qwen2.5:32b"
  formatting: "ollama/qwen2.5:32b"

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$0/month API cost

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Data stays local

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No rate limits

Cost Optimization Checklist

Assign Opus only to orchestration and complex reasoning

Save 30-50%

Use Sonnet for most subagents and cron jobs

Save 20-40%

Switch heartbeats to Haiku

Save 90% on heartbeats

Keep HEARTBEAT.md under 200 words

Save ~$3/month

Use QMD search instead of full file reads

Save 10-20%

Run data extraction on local models

Save 100% on those tasks

Schedule heavy jobs during off-peak hours

Lower latency

Monitor token usage weekly and optimize

Ongoing savings

Cut Your AI Costs in Half

Get weekly cost optimization strategies, model comparison updates, and pricing insights for AI builders.

Cost FAQ

How much does it cost to run an OpenClaw agent?

$100-750/month typical. Light personal use: $100-200. Business automation: $300-500. Heavy multi-agent systems: $500-1500. Optimization can reduce costs 40-60%.

What is model splitting?

Using expensive models (Opus) only for complex reasoning and cheaper models (Sonnet/Haiku) for routine tasks. An Opus orchestrator with Sonnet subagents gets 90% quality at 30% cost.

Can I use local models to reduce costs?

Yes. OpenClaw supports local models via Ollama. Use them for data extraction, classification, and formatting at zero API cost.

What's the cheapest useful setup?

Minimum viable: Sonnet for main agent + 3-5 cron jobs on existing hardware. About $100/month total. Useful for personal assistance and light automation.

Maximize Value, Minimize Cost

The best AI agent system is one you can afford to run forever. Optimize today.

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