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OpenClaw Cost Calculator

Estimate your monthly AI agent API costs. Compare Claude, GPT-5, and DeepSeek side-by-side.

1 Light (10-30) Moderate (50-100) Heavy (200+) 500

Combined input + output tokens. A typical OpenClaw conversation averages 1,500–3,000 tokens.

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What is OpenClaw?

OpenClaw is a free, open-source AI agent that runs locally on your machine. Unlike a simple chatbot, it acts as a proactive personal assistant — managing files, browsing the web, running commands, and automating workflows through platforms like WhatsApp, Telegram, and Discord.

While the software itself is free, the LLM API calls that power it are not. This calculator helps you estimate those recurring costs based on your provider, model, and usage habits.

Pay-Per-Token

LLM APIs charge per token (roughly ¾ of a word). You're billed separately for input tokens (your prompts) and output tokens (the AI's response). Output tokens are always more expensive.

Model Tiers

Every provider offers tiers: a flagship model (smartest, most expensive), a balanced mid-tier, and a fast/cheap tier. DeepSeek offers near-flagship quality at a fraction of the cost.

Hosting Costs

OpenClaw runs on your local machine (free) or a cheap VPS ($4-8/month). The API costs are the main recurring expense, which this calculator helps you estimate.

Cost Optimization

Use prompt caching (up to 90% savings on repeated context), batch API processing (50% discount), or switch to cheaper models for simple tasks to reduce your monthly bill.

Frequently Asked Questions

Yes, OpenClaw is 100% free and open-source software. However, you need to connect it to an LLM API (like Claude or GPT), which charges per-token fees. Those API costs are what this calculator estimates.
DeepSeek V3.2 is currently the cheapest option at $0.28/1M input and $0.42/1M output tokens. GPT-5 Nano ($0.05/$0.40) is also extremely affordable. For quality-to-cost ratio, Claude Haiku 4.5 and GPT-5 Mini are strong mid-range options.
A typical conversation (prompt + response) uses 1,500–3,000 tokens. Complex tasks like code generation or document analysis can use 5,000–10,000+ tokens per message. System prompts and persistent memory add overhead each time.
Yes! Use prompt caching (up to 90% savings on repeated context), the Batch API (50% discount for non-urgent tasks), or route simple tasks to cheaper models while keeping complex ones on premium tiers.
Generating output tokens requires more computation than processing input tokens. The model must predict each output token sequentially, consuming more GPU time and energy per token.

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