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name: model-savings description: > 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.

Model Savings

Quantify how much spend you would recover by moving eligible work to a cheaper model.

Input

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.

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 }] } — 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 |

Savings method

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_target

Pull 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.

Eligibility — don't promise savings on work that needs the big model

Re-pricing the full token mix is the theoretical ceiling. Scope it to eligible work:

• Low-turn sessions (metadata.turn_count small) and simple subagent/tool work are safe to downshift.
• Heavy-reasoning sessions (many thinking_blocks, high turn counts) likely need the premium model — exclude or discount them.
• Report both the full re-price (ceiling) and an eligible-only estimate, and state the eligibility rule you applied.

Report Sections

1. Current spend by model

Table from /api/pricing/cost: each model, its 4 token counts, and current cost. Note its share of total_cost.

2. Re-priced at target family

For each candidate, show cost_at_target and savings (absolute $ and %). Make the target rate card explicit.

3. Eligible-only estimate

Apply the eligibility rule and recompute savings over just the downshiftable token mix. Show how many sessions / what share of tokens qualified.

4. Recommended routing

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.

5. Caveats

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.

Output

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.