LLM Cost and Token Economics Modeler — Project Your Agent's Spend and Cut It in Half
Model what your LLM app or agent will cost, find where the money goes, and get a plan to cut it. Per-request and monthly projections, ranked cost drivers, an optimization plan with estimated savings, and unit economics against your pricing — with the arithmetic shown.
- Audit agent workflows to find hidden token multipliers and loops.
- Predict monthly LLM invoices based on user growth and call patterns.
- Compare the unit economics of different model tiers and routing strategies.
$14
· or 70 creditsSecure checkout via Stripe
Included in download
- Audit agent workflows to find hidden token multipliers and loops.
- Predict monthly LLM invoices based on user growth and call patterns.
- file_read, file_write automation included
- Ready for including Claude Code
PubsProToolkit builds rigor-first skills for AI agents — they write your docs and content properly, then adversarially r…
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See it in action
You say
Model the cost of our support agent. It runs on gpt-4o (assume $2.50 per 1M input tokens, $10 per 1M output tokens). A typical task takes about 5 turns; the system prompt is ~1,000 tokens, the user message ~500 tokens, and each turn re-sends the prior context and produces ~200 output tokens. We expect 1,000 users doing 10 tasks per day. Give me per-task and monthly cost, the top cost drivers, an optimization plan with estimated savings, and the unit economics against a $20/month plan — and show the arithmetic and assumptions.
Your agent does
Cost model for the gpt-4o support agent (estimate — calibrate against your own metering)
Assumptions used: gpt-4o at $2.50 / 1M input tokens and $10 / 1M output tokens (your figures); 5 turns per task; context re-sent each turn, so input grows by the prior 200-token output.
Per-request arithmetic:
- Turn 1 input 1,500; turn 2 1,700; turn 3 1,900; turn 4 2,100; turn 5 2,300 → 9,500 input tokens
- Output: 5 × 200 = 1,000 tokens
- Input cost: 9,500 / 1,000,000 × $2.50 = $0.0238
- Output cost: 1,000 / 1,000,000 × $10 = $0.0100
- Per-task cost ≈ $0.034
Monthly projection:
- Volume: 1,000 users × 10 tasks/day × 30 days = 300,000 tasks/month
- 300,000 × $0.034 ≈ $10,200 / month
Top cost drivers (ranked):
- Re-sent context — turns 2–5 resend the system prompt and history, making input ~70% of spend.
- Output tokens — $10/1M dominates per-token, ~29% of spend at this length.
- System-prompt overhead — the 1,000-token prompt is billed on every turn.
Optimization plan (each an estimate; test quality before shipping):
- Prompt caching on the static system prompt → est. ~$2,000–2,500/mo saved; low quality risk.
- Route turns 1–3 to a cheaper model (e.g., gpt-4o-mini) → est. ~$4,000–5,000/mo saved; test answer quality.
- Trim/summarize re-sent history after turn 2 → est. ~$1,500/mo saved; watch for lost context.
- Cap output length where full detail isn't needed → est. ~$800/mo saved.
Unit economics vs. a $20/month plan:
- Cost per user/month: 10 tasks/day × 30 × $0.034 ≈ $10.20
- Gross margin at $20: ($20 − $10.20) / $20 ≈ 49%
- Break-even usage: a flat $20 plan turns unprofitable above ~588 tasks/user/month (≈ 19–20/day).
Figures are estimates from the inputs you provided; the skill does not read your billing or meter live traffic.
LLM Cost and Token Economics Modeler — Project Your Agent's Spend and Cut It in Half
Model what your LLM app or agent will cost, find where the money goes, and get a plan to cut it. Per-request and monthly projections, ranked cost drivers, an optimization plan with estimated savings, and unit economics against your pricing — with the arithmetic shown.
$14
· or 70 creditsSecure checkout via Stripe
Included in download
- Audit agent workflows to find hidden token multipliers and loops.
- Predict monthly LLM invoices based on user growth and call patterns.
- file_read, file_write automation included
- Ready for including Claude Code
- Instant install
Media gallery
See it in action
You say
Model the cost of our support agent. It runs on gpt-4o (assume $2.50 per 1M input tokens, $10 per 1M output tokens). A typical task takes about 5 turns; the system prompt is ~1,000 tokens, the user message ~500 tokens, and each turn re-sends the prior context and produces ~200 output tokens. We expect 1,000 users doing 10 tasks per day. Give me per-task and monthly cost, the top cost drivers, an optimization plan with estimated savings, and the unit economics against a $20/month plan — and show the arithmetic and assumptions.
Your agent does
Cost model for the gpt-4o support agent (estimate — calibrate against your own metering)
Assumptions used: gpt-4o at $2.50 / 1M input tokens and $10 / 1M output tokens (your figures); 5 turns per task; context re-sent each turn, so input grows by the prior 200-token output.
Per-request arithmetic:
- Turn 1 input 1,500; turn 2 1,700; turn 3 1,900; turn 4 2,100; turn 5 2,300 → 9,500 input tokens
- Output: 5 × 200 = 1,000 tokens
- Input cost: 9,500 / 1,000,000 × $2.50 = $0.0238
- Output cost: 1,000 / 1,000,000 × $10 = $0.0100
- Per-task cost ≈ $0.034
Monthly projection:
- Volume: 1,000 users × 10 tasks/day × 30 days = 300,000 tasks/month
- 300,000 × $0.034 ≈ $10,200 / month
Top cost drivers (ranked):
- Re-sent context — turns 2–5 resend the system prompt and history, making input ~70% of spend.
- Output tokens — $10/1M dominates per-token, ~29% of spend at this length.
- System-prompt overhead — the 1,000-token prompt is billed on every turn.
Optimization plan (each an estimate; test quality before shipping):
- Prompt caching on the static system prompt → est. ~$2,000–2,500/mo saved; low quality risk.
- Route turns 1–3 to a cheaper model (e.g., gpt-4o-mini) → est. ~$4,000–5,000/mo saved; test answer quality.
- Trim/summarize re-sent history after turn 2 → est. ~$1,500/mo saved; watch for lost context.
- Cap output length where full detail isn't needed → est. ~$800/mo saved.
Unit economics vs. a $20/month plan:
- Cost per user/month: 10 tasks/day × 30 × $0.034 ≈ $10.20
- Gross margin at $20: ($20 − $10.20) / $20 ≈ 49%
- Break-even usage: a flat $20 plan turns unprofitable above ~588 tasks/user/month (≈ 19–20/day).
Figures are estimates from the inputs you provided; the skill does not read your billing or meter live traffic.
About This Skill
Most teams ship an LLM feature and meet its true cost on the invoice. Token spend is knowable in advance and controllable after — but only if someone models the actual call pattern instead of eyeballing a per-token price. A single agent task can fan out into dozens of calls, each dragging a growing context; a feature that's cheap per request can be ruinous per user. LLM Cost and Token Economics Modeler builds the model. Give it your architecture — models and prices, prompt and context sizes, the call pattern per user action including the hidden calls (tool loops, retries, subagents, re-sent history), and your volume — and it produces a per-request and monthly cost projection with the formula and assumptions shown, a ranking of what actually drives the bill, an optimization plan applying model routing, prompt-context trimming, caching, call-count reduction, and output discipline (each lever with an estimated saving and the quality trade-off to test), and the unit economics: cost per user versus your pricing, gross margin, and the usage level where a flat plan loses money. The download includes three reference files: the cost-model worksheet, the optimization-levers guide, and a complete worked example. Every figure is an estimate you can calibrate — it does the economics, it does not access your billing or meter live traffic, and model prices are yours to supply since they change. Works with Claude Code, Cursor, Codex CLI, Gemini CLI, and any SKILL.md agent.
Use Cases
- Audit agent workflows to find hidden token multipliers and loops.
- Predict monthly LLM invoices based on user growth and call patterns.
- Compare the unit economics of different model tiers and routing strategies.
- Calculate the break-even point for AI features on a flat-rate subscription.
Known Limitations
Every figure is an estimate whose accuracy depends on the token counts, call pattern, and model prices you provide. The skill does not access your billing or provider dashboards, connect to any API, meter or observe live traffic, or fetch current model prices — you supply prices, since they change. It does not automatically discover hidden calls; you describe the call pattern (tool loops, retries, subagents, re-sent history) and it models what you give it. Optimization savings are projected estimates, and each lever's quality trade-off must be tested in your own product. It does not modify your application code or implement the optimizations for you.
How to install
Drop the file into your AI tool. Works with Claude, Cursor, ChatGPT, and 20+ more.
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Permissions
File Scopes
This skill only reads the architecture details you provide and writes cost-model, optimization, and unit-economics documents plus the three reference files under references/**. It performs no network access: it does not call model providers, connect to billing or analytics APIs, fetch live prices, or meter traffic. The auto-detected host pubsprotoolkit.com was removed because the skill makes no external connections.
Tags
Works with any agent that follows the SKILL.md standard, including Claude Code, Cursor, Codex CLI, Gemini CLI, and VS Code Copilot. No runtime, build step, or installation required — it reads the architecture details you describe and writes Markdown deliverables plus the three reference files. You supply current model prices (they change over time); the skill does not fetch them.
Creator
PubsProToolkit builds rigor-first skills for AI agents — they write your docs and content properly, then adversarially review them to catch what's wrong before it ships. The result: cleaner output and a hard quality gate in one toolkit. Built by a CMPP-certified, PhD medical writer who brings regulated-industry standards to developer docs, content, compliance, and research integrity.
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