Break down Claude Code usage by model family (Opus / Sonnet / Haiku) from the Agent Monitor dashboard — each family's share of tokens, share of cost, and the spots where an expensive model is doing cheap work. Pulls per-model token and cost splits from /api/pricing/cost, current rates from /api/pricing, fleet token totals from /api/analytics, and per-session model assignment from /api/sessions. Use when deciding model routing or whether to downshift work to a cheaper tier.
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See where your tokens and dollars go by model family, and where to re-route work.
The user provides: $ARGUMENTS
This may be: empty (analyze the whole fleet), "today" / "this week" / a date range, or a focus like "where is Opus overused?". When empty, analyze all data from /api/pricing/cost and /api/sessions.
| Endpoint | Returns |
|---|---|
GET /api/pricing/cost | { total_cost, breakdown: [{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] } — per-model token and cost split |
GET /api/pricing | { pricing: [{ model_pattern, display_name, input_per_mtok, output_per_mtok, cache_read_per_mtok, cache_write_per_mtok }] } — rates per family |
GET /api/analytics | tokens totals (total_input, total_output, total_cache_read, total_cache_write — baselines pre-summed), agent_types for delegation context |
GET /api/sessions?limit=200 | Session list — model, cwd, started_at, ended_at, inline cost, metadata (JSON: thinking_blocks, turn_count, total_turn_duration_ms, usage_extras) |
Map each model in the cost breakdown to a family from its matched_rule / display_name:
| Family | Input $/Mtok | Output $/Mtok | Cache Read $/Mtok | Cache Write $/Mtok |
|---|---|---|---|---|
| Opus 4.5/4.6 | $5 | $25 | $0.50 | $6.25 |
| Sonnet 4/4.5/4.6 | $3 | $15 | $0.30 | $3.75 |
| Haiku 4.5 | $1 | $5 | $0.10 | $1.25 |
cost = (tokens / 1M) × rate_per_mtok summed over the 4 token types; longest model_pattern wins. Opus output costs ~5× Sonnet and ~5× Haiku per token, so a family's cost share routinely exceeds its token share — that gap is the routing signal.
Aggregate input + output + cache_read + cache_write tokens per family from /api/pricing/cost. Show each family's tokens and percent of total. Cross-check the grand total against /api/analytics token totals.
Sum cost per family. Show each family's dollar total and percent of total_cost. Place the cost-share % next to the token-share % so the premium gap is visible.
For each family compute cost_share − token_share. A large positive gap on Opus/Sonnet signals premium spend concentration. Rank families by gap.
From /api/sessions?limit=200, find Opus/Sonnet sessions with signals of low complexity: low turn_count, short total_turn_duration_ms, few thinking_blocks, or small token footprints. List candidates that could plausibly run on a cheaper tier, with current cost and estimated cost if downshifted.
Structured Markdown with tables. Currency as USD to 4 decimal places; rates as $/Mtok; token shares and cost shares as percentages; use ▲/▼ for the cost-vs-token gap and any trend. Token counts with thousands separators.
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Inspect fired CCAM alerts and manage alert rules for token thresholds, event patterns, inactivity, and status duration. Use when acknowledging alerts, creating or editing a rule, checking cooldowns, or connecting alert rules to webhook targets.
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Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error propagation anomalies), and sessions with abnormal metadata (extreme turn_count, high thinking_blocks, zero turn_duration).
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Produce a detailed report on APIError events from Agent Monitor data — counts over time, which sessions and models are affected, and the likely root cause (rate limits, overload/529, or context-window pressure) inferred from each event's summary and data payload. Use when API errors spike or when you need to explain why requests are failing.
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Benchmark one session (or a small recent set) against the rolling average using Agent Monitor data — cost, total tokens, tool count, and workflow complexity score — and report where each metric lands as a percentile of the population. Tells you whether a session was normal, cheap, or an outlier. Use when judging whether a session was typical or out of band.
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Define a spend budget for Claude Code and, optionally, create a cost alert rule that fires when usage crosses the limit, via POST /api/alerts/rules on the Agent Monitor dashboard. Reads current spend from /api/pricing/cost to size the budget sensibly and explains every rule field before writing. Use when setting a spend limit or wiring up a budget guardrail.
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Analyze prompt-cache effectiveness for Claude Code usage from the Agent Monitor dashboard — cache hit rate (total_cache_read / (total_cache_read + total_input)), cache_write vs cache_read reuse, cache-read vs cache-write spend, and the sessions with the poorest reuse. Pulls token totals from /api/analytics, per-session detail from /api/sessions, and dollar splits from /api/pricing/cost. Use when diagnosing cache spend or deciding whether prompt caching is paying off.
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