AI cost calculator · Rates verified July 22, 2026
What AI will actually cost your company.
Describe the work your team does in plain English. The calculator estimates monthly spend across four leading models, routes each job to the cheapest model good enough, and shows what disciplined caching saves.
01 · Inputs
Describe the company and the work.
Start with headcount and industry shape. Then keep, remove, or resize the work categories that resemble your actual operating load.
Monthly work volumes scale from a baseline of 100 employees.
General business reduces software-development volume by two-thirds.
Each job routes to the cheapest independently graded model that clears this bar.
Work your team does
Monthly operating load
Tick the jobs you use AI for. Volumes are editable; expand a row to inspect token assumptions.
02 · Results
One number first. The tradeoffs underneath.
The headline is the routed monthly cost at your current settings. Secondary figures stay compact; the detailed route remains available for scrutiny.
Cost comparison
Optimized routing versus one model for everything.
Compare the cheapest model good enough for each job with a premium default and the cheapest model regardless of quality.
Routed mix
Which job goes where—and why.
Every cell shows monthly cost, quality tier, and estimated turnaround. The recommendation is the cheapest eligible result, not the cheapest token price.
| Job | Tasks / mo | Fable 5 | Sol | Kimi K3 | Qwen | Recommendation |
|---|
Caching discipline
What prompt reuse is worth.
The same routed workload, priced with typical unmanaged caching versus a well-configured setup.
Cost per completed task
Why list price lies.
A cheap token is not a cheap job when a model burns several times the tokens to finish equivalent work.
| Model | List $/task | Adjusted $/task | vs. list | Measured anchor | Source |
|---|
AdvancedAssumptions, alternate views, charts, and rate card
Three-strategy cost
Caching sensitivity
Break-even analysis
When does Kimi K3 stop being the cheapest?
Solves for the efficiency multiplier where each rival becomes cheaper across the whole routed mix.
| Rival model | Rival monthly | Break-even k | Current k | Status |
|---|
Rate card
Edit assumptions without changing the model.
Prices are dollars per one million tokens. Efficiency multipliers describe how many times the raw token count each model uses to complete equivalent work.
| Model | In | Cached in | Out | Eff lo | Eff mid | Eff hi |
|---|
Methodology & sources
Rates: Anthropic, OpenAI, Moonshot AI, and Qwen published pricing or stated preview subscription tiers, verified July 22, 2026.
Efficiency: Artificial Analysis and vendor-reported agentic benchmarks where available; assumptions remain explicitly labeled in the tables.
Quality: Artificial Analysis, vals.ai, EQ-Bench, BrowseComp, OmniDocBench, and LegalBench. Human review remains mandatory for legal and financial work.
Caching: Unmanaged assumes roughly 10% cache hits. Well-configured rates vary by job shape; theoretical max never exceeds the reusable token share.
From estimate to operating plan
The math is useful. The operating decision is the real work.
Use the AI-readiness diagnostic to identify which workflows deserve the first investment—and what has to change around them for the economics to hold.