LLM token cost estimator
Monthly $ ≈ (runs × tokens / 1e6) × price per million. Prices are yours to type.
Estimate monthly model spend from your numbers
Token invoices scale with runs and context size. Enter your volume and the $/M rates from your provider’s current pricing page — this estimator does not invent a rate card.
How to use the LLM token cost estimator
Set monthly runs, average input and output tokens per run, and input/output price per million tokens. The total is an estimate at that volume, not a quote from a vendor.
Your volume and prices
$2.10
per month at that volume.
What the estimate means
Treat it as a planning number. Re-check when the provider changes prices, when prompts grow, or when you add retrieval. Pair with automation ROI for the full business case.
FAQ
How does this LLM token cost estimator work?
Monthly cost ≈ (runs × input tokens / 1,000,000) × $/M input + (runs × output tokens / 1,000,000) × $/M output. You type the prices — the tool does not embed vendor rate cards.
Where should I get $/M token prices?
From your model provider’s published pricing for the exact model and region you use (for example OpenAI, Anthropic, or Google AI pricing pages). Prices change; paste the current numbers rather than relying on a cached table here.
Why separate input and output prices?
Most providers bill input and output tokens at different rates. Agent runs that retrieve long context can be input-heavy; runs that draft long replies can be output-heavy. Separating them keeps the estimate closer to the invoice.
Does this include tools, embeddings, or fine-tuning?
No. It only multiplies the token volumes and prices you enter. Add embedding, tool, or hosting costs separately when you build a full automation ROI case.
How can an agentic workflow reduce token spend?
Shorter prompts, smaller retrieved chunks, caching where the provider supports it, and routing easy steps to cheaper models. Pair this estimator with knowledge-base chunk sizing so RAG context stays bounded.
Need agentic workflows with bounded model spend?
GoWith agentic builds size context and retries so token cost stays predictable. See all free tools, the automation ROI calculator, the knowledge-base chunk size estimator, and the retry / backoff calculator.
Educational only. Not legal advice and not a substitute for testing the live agent. LLM token cost estimator runs in your browser — nothing is sent to your CRM unless you copy it yourself.