LLM CostCalculator

Are you overpaying for your AI? Let's find out.

Tell the LLM Cost Calculator what you run and at what volume. It compares per-token API pricing for open-weight models against your current provider and gives you a clear recommendation to stay, pilot or switch. Subscription ChatGPT/Claude plans aren’t the comparison — this is about what you pay per request.

Matches your usage — spend, requests, or raw tokens — and your use case against a benchmark of open-weight models, then scores each candidate on projected savings and the quality gap to your incumbent.

Pick your current model, enter your volume, describe your use case (or let the classifier suggest one), and set your quality bar. The LLM Cost Calculator returns a ranked shortlist, projected monthly savings, and the verdict with the reasoning behind it.

It isn’t a benchmark itself and it won’t run your prompts. Verdicts are projections from public head-to-head data — treat a switch as grounds for a pilot, not a migration plan.

Open-weight models like GLM, DeepSeek and Qwen now sit within a few benchmark points of frontier closed models at a fraction of the per-token API cost — with no vendor lock-in, no per-seat fees, and the option to self-host. The savings bar below is what flips the decision.

01Your setup02Your use case03The verdictMethodology →
01
Your setup
The model you run today and how much you use it.
Closed, hosted models only — that’s your baseline.
$/ month
We convert to a common basis to compare like-for-like.
50%
Prompt caching, batch, and negotiated rates usually discount real bills vs list — agentic workflows typically run 60–70% cache rates. Set 0 if you pay full list. Candidates are compared at list price, so the savings shown are conservative.
Shapes the “Cost-efficient” pick and the stay / pilot / switch verdict — how aggressively to trade quality for savings.
02
Your use case
Pick categories, or describe the task — either is enough.
Weighted 70 / 30 across primary and secondary.
or
Free · instant · numbers are deterministic