memex/projects/token-optimization/plan.org

Token Optimization Strategy

• Executive Summary
  • Goal: Minimize inference costs while maximizing capability
• Three-Tier Model Strategy
  • Tier 1: Fast/Cheap (80% of queries)
  • Tier 2: Balanced (18% of queries)
  • Tier 3: High-Performance (2% of queries)
• Provider Analysis
  • Google AI Studio (Primary Recommended)
  • OpenRouter.Aggregated (Secondary)
  • OpenAI (Tier 3 only)
  • Local Inference (Long-term goal)
• Context Optimization Strategies
  • 1. Context Windows by Task Type
  • 2. Conversation Pruning
  • 3. RAG vs. Full Context
• Request Optimization
  • Batching Strategy
  • Caching Strategy
  • Streaming vs. Non-Stream
• Smart Routing Rules
  • Automatic Selection Logic
  • Fallback Chain
• Concrete Implementation
  • Config Structure (openclaw.json)
  • Monitoring & Alerts
• Cost Projections
  • Current Unknown Usage → Optimized
• Immediate Actions
  • Week 1: Setup
  • Week 2: Implement
  • Week 3: Optimize
  • Week 4: Scale
• Long-term: Local Inference Path
  • Minimum Viable Setup
  • Full Self-Hosted

Executive Summary

Goal: Minimize inference costs while maximizing capability

Current approach: Single default model → Multi-tier, multi-provider strategy

Three-Tier Model Strategy

Tier 1: Fast/Cheap (80% of queries)

• Purpose: Simple tasks, formatting, lookups
• Models: Google Gemini Flash, Local models
• Cost: $0-0.000001 per 1K tokens
• Speed: Fastest

Tier 2: Balanced (18% of queries)

• Purpose: Complex reasoning, code generation, analysis
• Models: Gemini Pro, Claude Haiku, Llama 3 70B
• Cost: $0.0001-0.003 per 1K tokens
• Speed: Medium

Tier 3: High-Performance (2% of queries)

• Purpose: Critical decisions, complex architecture, final review
• Models: GPT-4, Claude Opus, Gemini Ultra
• Cost: $0.01-0.03 per 1K tokens
• Speed: Slower

Provider Analysis

Google AI Studio (Primary Recommended)

Model	Free Tier	Rate Limit	Best For
Gemini 2.0 Flash	300K tokens/day	60 req/min	Quick tasks, coding
Gemini 1.5 Flash	300K tokens/day	60 req/min	Fast responses
Gemini 1.5 Pro	300K tokens/day	60 req/min	Complex tasks

Cost: FREE (within limits)

OpenRouter.Aggregated (Secondary)

Model	Price/1K tokens	Context	Reliability
Qwen 3 235B	$0.0001-0.0003	128K	High
Mistral Large	$0.002-0.006	128K	High
Llama 4 405B	$0.0002-0.0005	128K	Medium
Free tier models	$0	Varies	Variable

OpenAI (Tier 3 only)

• GPT-4: $0.03/1K tokens (expensive)
• GPT-4o: $0.005/1K tokens (better value)
• Use sparingly for critical tasks only

Local Inference (Long-term goal)

• Hardware: $1000-5000 initial investment
• Ongoing: $0 (electricity only)
• Models: Llama 3, Mistral, DeepSeek
• Best for: High-volume, privacy-sensitive work

Context Optimization Strategies

1. Context Windows by Task Type

Task Type	Optimal Context	Compression	Savings
Code review	4K-8K	Truncate old files	50%
Documentation	8K-16K	Summarize sections	30%
Research	16K-32K	Chunk + RAG	70%
Architecture	32K-128K	Maintain full	0%

2. Conversation Pruning

• Remove "thinking" blocks from history
• Summarize conversation every 10 turns
• Archive old sessions to external storage

3. RAG vs. Full Context

• Rule: < 5K tokens of context → Full
• Rule: > 10K tokens of context → Use embeddings/RAG
• Savings: 60-80% on large document tasks

Request Optimization

Batching Strategy

• Group similar requests (3-5 per batch)
• Same model, same parameters
• Shared overhead costs

Caching Strategy

• Cache embeddings for repeated contexts
• Store common completions (templates)
• Reuse code snippet suggestions

Streaming vs. Non-Stream

• Streaming: Better UX, but higher token overhead
• Non-stream: More efficient for programmatic use
• Recommendation: Non-stream for background tasks

Smart Routing Rules

Automatic Selection Logic

``` IF task_type == "simple_lookup" OR "formatting": → Gemini Flash (free)

ELIF task_type == "code_generation" AND complexity < 3: → Gemini Pro (free tier)

ELIF task_type == "complex_reasoning" OR "architecture": → Claude Sonnet or GPT-4o

ELIF task_type == "final_review" OR "critical_decision": → GPT-4 or Claude Opus ```

Fallback Chain

1. Try Gemini (free)
2. If rate limited → OpenRouter (cheap)
3. If quality insufficient → GPT-4o
4. If critical failure → GPT-4

Concrete Implementation

Config Structure (openclaw.json)

```json { "models": { "defaults": { "primary": "google-gemini-cli/gemini-2.0-flash", "fallbacks": [ "openrouter/qwen/qwen3-235b-a22b", "google-gemini-cli/gemini-1.5-pro", "openai/gpt-4o" ] }, "providers": { "google-gemini-cli": { "freeTier": true, "dailyLimit": 300000, "rateLimit": 60 }, "openrouter": { "freeTierModels": ["openrouter/auto"], "budgetLimit": 500 }, "openai": { "budgetLimit": 200, "useFor": ["critical", "architecture"] } } } } ```

Monitoring & Alerts

• Track daily token usage per provider
• Alert at 80% of free tier limits
• Monthly budget review and adjustment

Cost Projections

Current Unknown Usage → Optimized

Scenario	Monthly Tokens	Current Cost	Optimized Cost	Savings
Light (< 1M)	1M	$50-100	$0-10	90%
Medium (1-5M)	3M	$200-500	$20-100	80%
Heavy (5-20M)	10M	$1000-3000	$200-500	80%

Immediate Actions

Week 1: Setup

• Configure Gemini as primary provider
• Set up OpenRouter fallback
• Implement basic usage tracking
• Document current baseline

Week 2: Implement

• Add smart routing logic
• Implement context compression
• Set up budget alerts
• A/B test model choices

Week 3: Optimize

• Analyze usage patterns
• Fine-tune routing rules
• Tune context windows
• Document findings

Week 4: Scale

• Full multi-provider setup
• Implement full caching
• Maximize free tier usage
• Plan for paid tiers if needed

Long-term: Local Inference Path

Minimum Viable Setup

• Hardware: RTX 4090 or Apple Silicon M3 Max
• Software: Ollama + OpenClaw integration
• Cost: ~$2000-4000 one-time
• Break-even: 3-6 months vs. API costs

Full Self-Hosted

• Hardware: Dual RTX 4090 or 2x Mac Studio
• Models: Llama 3 70B, Mixtral 8x22B
• Cost: ~$8000-12000
• For: Privacy, unlimited inference, control