| name | cost-management |
| description | Claude API cost awareness — token estimation, cost drivers, and efficiency strategies for Claude Code sessions |
Cost Management — Claude API Token Awareness
When to Activate
- User asks about Claude API costs or token usage
- Session shows unexpectedly high tool call volume
- Setting up a new project where cost matters
- Planning long autonomous sessions or overnight pipelines
- Choosing between Haiku, Sonnet, and Opus for a specific task or agent role
- Debugging why a particular workflow consumed more tokens than expected
- Designing a multi-agent pipeline where per-call cost adds up quickly
Why This Matters
Real-world data from Claude Code users:
- Aider users: "$35–40 in a few days" without noticing (Issue #605)
- Cline users: "$50/day without realizing" (Discussion #1727)
- One intensive session: 100–300k tokens = $0.30–$4.50 with Claude Sonnet
Token visibility is the #1 requested feature across all major AI coding tools.
clarc tracks this automatically via the session-end hook.
Model Cost Reference (2026)
| Model | Input | Output |
|---|
| Claude Haiku 4.5 | ~$0.25 / M tokens | ~$1.25 / M tokens |
| Claude Sonnet 4.6 | ~$3.00 / M tokens | ~$15.00 / M tokens |
| Claude Opus 4.6 | ~$15.00 / M tokens | ~$75.00 / M tokens |
Prices subject to change. Always verify at console.anthropic.com.
Rule of thumb: Output tokens cost 5× more than input tokens. Minimize verbose output.
Cost Drivers — What Makes Sessions Expensive
1. Reading large files completely
EXPENSIVE: Read entire 2000-line file to find one function
CHEAP: Grep for the function, then Read only the relevant 20 lines
2. Agent cascades
Each agent call = new context window = new cost.
5 sequential agents on a large codebase can easily cost $1–2.
3. Images and screenshots in context
Vision inputs are input-token-heavy. One screenshot ≈ 1,000–5,000 tokens.
4. Long sessions without /compact
Context accumulates. A 4-hour session without /compact may carry 200k+ tokens
of prior context into every new tool call. (/compact is a built-in Claude Code command, not a clarc command.)
5. Wide glob patterns
Glob **/* on a large repo returns thousands of paths — all as input tokens.
Efficiency Strategies
Grep before Read
Instead of loading an entire file, locate the relevant lines first:
// EXPENSIVE — loads all 500 lines into context:
Read { file_path: "src/api/users.ts" }
// CHEAP — finds the line number first, then reads only 30 lines:
Grep { pattern: "getUserById", path: "src/api/", output_mode: "content", -n: true }
// → src/api/users.ts:47:export async function getUserById(id: string) {
Read { file_path: "src/api/users.ts", offset: 47, limit: 30 }
On a 500-line file this saves ~470 lines of input tokens per lookup. Across a 50-file session the savings compound to tens of thousands of tokens.
Delegate to Haiku for simple tasks
Haiku is ~8× cheaper than Sonnet. Use it for:
- Summarization
- Simple transformations
- Extracting structured data from text
- Writing boilerplate
Use /compact proactively
Run /compact (built-in Claude Code command) when context > 60% full. The summary costs ~$0.01 and saves
much more in subsequent calls.
Scope control
Clear task boundaries prevent scope creep. "Fix the null check in getUserById"
is 10× cheaper than "review the whole auth module."
Agent isolation
Agents protect the main context. A Haiku sub-agent doing file analysis
costs far less than loading all those files into the Sonnet main context.
Cost Tracking in clarc
clarc automatically logs estimated session costs to ~/.clarc/cost-log.jsonl.
tail -5 ~/.clarc/cost-log.jsonl | jq .
/session-cost
Example ~/.clarc/cost-log.jsonl entries:
{"date":"2026-03-12","session_id":"ses_abc123","model":"claude-sonnet-4-6","tool_calls":{"Read":14,"Grep":8,"Edit":6,"Bash":4,"Agent":1},"est_input_tokens":42000,"est_output_tokens":8500,"est_cost_usd":0.25,"duration_min":22}
{"date":"2026-03-12","session_id":"ses_def456","model":"claude-sonnet-4-6","tool_calls":{"Read":31,"Grep":5,"Edit":12,"Bash":9,"Agent":3},"est_input_tokens":98000,"est_output_tokens":21000,"est_cost_usd":0.61,"duration_min":51}
{"date":"2026-03-11","session_id":"ses_ghi789","model":"claude-opus-4-6","tool_calls":{"Read":8,"Grep":3,"Edit":2,"Bash":2,"Agent":0},"est_input_tokens":18000,"est_output_tokens":4200,"est_cost_usd":0.59,"duration_min":14}
Key fields: tool_calls shows which tools drove cost; est_cost_usd is the session estimate; Agent calls are the most expensive per-call (each spawns a new context window).
Important: These are estimates based on tool-call count heuristics.
For exact costs, check console.anthropic.com → Billing.
When to Use Which Model
| Task | Model | Why |
|---|
| Code review, TDD, standard dev | Sonnet | Best quality/cost ratio |
| Lightweight analysis, summaries | Haiku | ~8× cost savings |
| Architecture decisions, complex debugging | Opus | Deep reasoning needed |
| Worker agents in pipelines | Haiku | High volume, lower stakes |
| Orchestrator in multi-agent | Sonnet | Coordination complexity |
Related
/session-cost — view session cost summaryscripts/hooks/auto-checkpoint.js — checkpoint before expensive operationsskills/cost-aware-llm-pipeline — designing cost-efficient multi-agent pipelines
