LLM Council Analysis Reveals Practical Claude Code Token Optimization Strategies
Problem and Experiment Setup
A developer experiencing daily Claude Code usage limits conducted an experiment using LLM Council (https://github.com/karpathy/llm-council). The setup involved 5 different personas that were forced to critique, challenge, and refine solutions, followed by a peer review round.
Key Findings
The analysis revealed that the biggest token drain wasn't complexity, but using "thinking mode" by default. This alone was burning tokens almost like Opus.
Practical Optimization Habits
- Turn OFF extended thinking by default
- /clear after every git commit (non-negotiable)
- Stop writing "yes / continue" prompts
- /compact every ~40 messages
- Keep CLAUDE.md lean or you pay tax every session
Mental Shift and Results
The core insight: Stop treating intelligence as default. Treat it like a resource you deploy intentionally. This shift enables:
- 30-50% token savings instantly
- Ability to actually use Opus without fear
- Predictable daily workflow instead of random limit hits
The council emphasized one rule: If you don't track /cost, you're not optimizing... you're guessing.
Outcome
With the full playbook implementation:
- ~60-70% reduction in token usage
- Same or better output quality
- Opus becomes usable for high value work
The developer noted this approach was more effective than any single prompt hack.
📖 Read the full source: r/ClaudeAI
👀 See Also
LiteParse: Fast Open-Source Document Parser for AI Agents
LiteParse is an open-source document parser that provides spatial text parsing with bounding boxes, runs locally without GPUs, and supports PDFs, Office documents, and images. It can be installed as a skill for 40+ AI agents including Claude Code, Cursor, and OpenClaw.
Spec-Driven Development Workflow for Claude Code: Decomposition, Context Clearing, and Cost Control
A spec-driven development approach for Claude Code that uses two-dimensional decomposition, context clearing between steps, and specs written to disk to improve agent performance and reduce costs.
Project Headroom: Netflix Engineer's Open Source Tool Slashes AI Token Costs by 90%
Netflix senior engineer Tejas Chopra created Project Headroom, an open source proxy that compresses AI context input by up to 90%, saving an estimated $700,000 across users since January 2026. It runs locally on port 8787 and wraps any LLM CLI.
Persistent Memory for Claude: Local Stack with MCP, 39ms Retrieval, 82% Token Reduction
A developer built a persistent memory layer for Claude using local vector search (Qdrant + Qwen3) and MCP integration, achieving 82% token reduction, 39ms hot-path retrieval, and session crystallization via L4 nodes.