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Get Started Free →Estimate the dollars saved by routing eligible Claude Code work to a cheaper model family, using the Agent Monitor pricing engine. Re-prices each model's token mix at the target family's rates and quantifies the delta. Uses /api/pricing (rates), /api/pricing/cost (current per-model spend), /api/sessions, and /api/analytics. Use when hunting for cost cuts or comparing model tiers.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | -19% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 34% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -23% | 0% |
| case-15 | ✗→✓ | ▲ Improved | -10% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -13% | 0% |
Quantify how much spend you would recover by moving eligible work to a cheaper model.
The user provides: $ARGUMENTS
This is the routing question — e.g. "Opus → Sonnet", "move simple work to Haiku", or empty (analyze every premium model against the next tier down). If no target family is named, default to proposing the next-cheaper tier per model and say so.
| Endpoint | Returns | |----------|---------| | GET /api/pricing | { pricing: [{ model_pattern, display_name, input_per_mtok, output_per_mtok, cache_read_per_mtok, cache_write_per_mtok }] } — the rate card for every family | | GET /api/pricing/cost | { total_cost, breakdown: [{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] } — current spend and the exact token mix per model | | GET /api/sessions?limit=200 | Sessions with model, inline cost, and metadata (turn_count, thinking_blocks) — used to judge which work is eligible to downshift | | GET /api/analytics | agent_types, tool_usage, total_subagents — corroborate which task types are low-complexity and safe to route cheaper |
For each candidate model in the cost breakdown, re-price its exact token mix at the target family's rates:
cost_at_target = (input_tokens / 1M) × target.input_per_mtok
+ (output_tokens / 1M) × target.output_per_mtok
+ (cache_read_tokens / 1M) × target.cache_read_per_mtok
+ (cache_write_tokens/ 1M) × target.cache_write_per_mtok
savings = current_model_cost − cost_at_targetPull target.*_per_mtok from /api/pricing (longest model_pattern match wins). Default rates ($/Mtok in/out/cacheRead/cacheWrite): Opus $5/$25/$0.50/$6.25, Sonnet $3/$15/$0.30/$3.75, Haiku $1/$5/$0.10/$1.25.
Re-pricing the full token mix is the theoretical ceiling. Scope it to eligible work:
metadata.turn_count small) and simple subagent/tool work are safe to downshift.Table from /api/pricing/cost: each model, its 4 token counts, and current cost. Note its share of total_cost.
For each candidate, show cost_at_target and savings (absolute $ and %). Make the target rate card explicit.
Apply the eligibility rule and recompute savings over just the downshiftable token mix. Show how many sessions / what share of tokens qualified.
Rank routing moves by eligible monthly savings (descending), top 5. For each: source → target, the token mix moved, estimated $ saved, and a confidence level (high/medium/low) based on how clearly the work is low-complexity.
Cheaper models may need more turns or produce more output — note that realized savings can be lower than the static re-price, and that quality-sensitive work should stay on the premium tier.
Markdown tables. Currency as USD to 4 decimal places; token counts with thousands separators; rates as $/Mtok. Always present both the ceiling (full re-price) and the eligible-only estimate so the number is honest.
Other measured skills in the registry, with their headline benchmark lift.