cost-breakdown

# Cost Breakdown Detailed cost analysis from the Agent Monitor's pricing engine. ## Input The user provides: **$ARGUMENTS** This may be: "today", "this week", "last 30 days", a session ID, or "budget $50/week". ## Data Sources | Endpoint | Returns | |----------|---------| | `GET /api/pricing` | `{ pricing: [{ model_pattern, display_name, input_per_mtok, output_per_mtok, cache_read_per_mtok, cache_write_per_mtok }] }` | | `GET /api/pricing/cost` | Total cost: `{ total_cost, breakdown: [{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] }` | | `GET /api/pricing/cost/{sessionId}` | Per-session cost with same breakdown shape | | `GET /api/sessions?limit=200` | Sessions list — each includes inline `cost` field (bulk pricing) | | `GET /api/analytics` | Token totals (total_input, total_output, total_cache_read, total_cache_write — baselines pre-summed), daily trends | ### How costs are calculated The pricing engine matches model names against `model_pattern` using SQL LIKE (e.g. `claude-sonnet-4-5%` matches `claude-sonnet-4-5-20250514`). **Longest pattern wins** for specificity. Cost per model: ``` cost = (input_tokens / 1M) × input_per_mtok + (output_tokens / 1M) × output_per_mtok + (cache_read_tokens / 1M) × cache_read_per_mtok + (cache_write_tokens / 1M) × cache_write_per_mtok ``` Token counts are **effective totals** = `current + baseline` (baselines preserve pre-compaction tokens that would otherwise be lo...