markus41/plugins/claude-code-expert/archive/v7.6.0/skills/cost-optimization

Claude Code Cost Optimization

Complete guide to managing costs, model routing, token usage, and caching.

Cost Tracking

/cost Command

/cost

Shows:

• Input tokens consumed
• Output tokens consumed
• Cache read tokens (cheaper)
• Cache write tokens
• Total estimated cost (USD)

Model Selection & Routing

Available Models

| Model | ID | Best For | Cost | |-------|-----|---------|------| | Opus 4.6 | claude-opus-4-6 | Architecture, complex decisions | Highest | | Sonnet 4.6 | claude-sonnet-4-6 | General development, implementation | Medium | | Haiku 4.5 | claude-haiku-4-5-20251001 | Quick lookups, simple tasks | Lowest |

Switching Models

/model claude-haiku-4-5-20251001   # Switch to Haiku for simple tasks
/model claude-sonnet-4-6            # Switch back to Sonnet
/model claude-opus-4-6              # Switch to Opus for complex work

CLI Model Override

claude -m claude-haiku-4-5-20251001 -p "quick question"

Settings Configuration

{
  "model": "claude-sonnet-4-6",
  "smallFastModel": "claude-haiku-4-5-20251001"
}

Token Reduction Strategies

1. Use /compact Frequently

/compact                    # Compress full conversation
/compact focus on the API   # Compress with specific focus

Reduces context window size, lowering per-message input costs.

2. Targeted File Reads

// Expensive: read entire large file
Read(file_path="large-file.ts")        // ~5000 tokens

// Cheap: read specific section
Read(file_path="large-file.ts", offset=100, limit=30)  // ~300 tokens

// Cheap: search first
Grep(pattern="function auth", path="src/")  // ~100 tokens

3. Use Sub-Agents for Research

Sub-agents process information internally and return summaries:

// Main context gets only the summary (~500 tokens)
// Instead of 20 file reads (~50,000 tokens)
Agent(subagent_type="Explore", prompt="Find all database models")

4. Grep Before Read

// Don't read every file looking for something
// Search first, then read only matching files
Grep(pattern="TODO|FIXME", type="ts")

5. Background Tasks

// Long tasks don't consume main context while running
Agent(run_in_background=true, ...)
Bash(command="npm test", run_in_background=true)

6. Clear Between Unrelated Tasks

/clear   # Reset context for new topic

Prompt Caching

How It Works

• Claude Code caches system prompts and conversation history
• Cached tokens cost significantly less (~90% savings)
• Cache hits happen when the same prefix appears in consecutive requests

Maximizing Cache Hits

1. Keep CLAUDE.md stable — Changes invalidate cache
2. Consistent system prompts — Don't modify with --append-system-prompt frequently
3. Sequential conversations — Cache benefits multi-turn conversations

API-Level Caching

const response = await client.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 1024,
  system: [
    {
      type: "text",
      text: "Your system prompt here...",
      cache_control: { type: "ephemeral" }
    }
  ],
  messages: [...]
});

// Usage shows cache info
console.log(response.usage.cache_creation_input_tokens);
console.log(response.usage.cache_read_input_tokens);

Provider Cost Comparison

Anthropic Direct

Standard pricing, most features.

AWS Bedrock

CLAUDE_CODE_USE_BEDROCK=1 claude

• May have different pricing through AWS agreements
• Cross-region inference available
• Committed use discounts possible

Google Vertex AI

CLAUDE_CODE_USE_VERTEX=1 claude

• GCP pricing and billing
• May have committed use discounts

Batch Processing (50% Savings)

For non-interactive workloads, use the Message Batches API:

const batch = await client.messages.batches.create({
  requests: [
    {
      custom_id: "review-1",
      params: {
        model: "claude-sonnet-4-6",
        max_tokens: 1024,
        messages: [{ role: "user", content: "Review file1.ts" }]
      }
    },
    {
      custom_id: "review-2",
      params: {
        model: "claude-sonnet-4-6",
        max_tokens: 1024,
        messages: [{ role: "user", content: "Review file2.ts" }]
      }
    }
  ]
});

Batch processing gives 50% cost reduction with 24-hour SLA.

Cost Estimation

Rule of Thumb

| Task | Approximate Cost | |------|-----------------| | Simple question | $0.01 - $0.05 | | Code review (1 file) | $0.05 - $0.15 | | Feature implementation | $0.20 - $1.00 | | Complex refactoring | $0.50 - $2.00 | | Full project analysis | $1.00 - $5.00 |

Factors Affecting Cost

1. Model used (Opus > Sonnet > Haiku)
2. Context window size (more history = more input tokens)
3. Number of tool calls (each adds output + input)
4. File sizes read
5. Number of conversation turns
6. Extended thinking budget

Best Practices

1. Start with Sonnet — Good balance of quality and cost
2. Use Haiku for exploration — Switch for simple lookups
3. Upgrade to Opus for architecture — Worth the cost for complex decisions
4. Compact regularly — Smaller context = lower cost per turn
5. Delegate research — Sub-agents are cost-neutral for main context
6. Monitor with /cost — Track spending periodically
7. Use batch API for bulk — 50% savings on non-interactive work
8. Leverage caching — Keep system prompts stable