How NadirClaw Saves 40-70% on LLM API Costs
Most LLM API costs are wasted on simple requests. Status checks, formatting tasks, basic Q&A, and heartbeat pings all get routed to expensive models like Claude Opus or GPT-4 — even when a cheaper model would produce identical results.
Real-time complexity analysis
NadirClaw analyzes each prompt's complexity in real-time using lightweight classification. Simple prompts route to cost-efficient models (Claude Haiku, GPT-4o-mini) while complex tasks like code generation and multi-step reasoning stay on premium models. The routing is transparent to your application — NadirClaw sits as a proxy, so no code changes are required.
How the routing works
Every incoming request passes through NadirClaw's complexity analyzer, which classifies prompts into four tiers: Simple, Complex, Reasoning, and Agentic. Each tier maps to a set of models optimized for that level of difficulty. The classification adds roughly 10ms of latency — negligible compared to the LLM response time itself.
Real-world savings
In real-world multi-agent setups running Claude Code, Cursor, or Aider, we see consistent 40-70% cost reductions without any degradation in output quality for complex tasks. The savings come from the fact that a large percentage of API calls in typical workflows are simple operations that don't need a premium model.
Getting started
Install NadirClaw with pip install nadirclaw, set your API keys, and point your LLM client's base URL to NadirClaw. That's it — your existing code works exactly the same, but your bill shrinks.