AI Cost per 1,000 Requests Calculator
Estimate the API cost of 1,000 requests across GPT, Claude, Gemini, Mistral and other leading models using your average input and output token size.
Estimate your workload
Change the assumptions on the left; read the modeled result on the right.
Compare the same 1,000 requests across workloads
Keep request count fixed at 1,000, then vary the average input and output size for each application.
1,000 chatbot requests
Model short support or product conversations with your expected prompt and response length.
1,000 agent requests
Use a larger token assumption when one user task triggers multi-step reasoning or repeated model calls.
1,000 summarization requests
Increase input tokens for long documents and compare how model choice changes the unit cost.
How to read this estimate
What this calculator is for
Cost per thousand requests is a practical bridge between engineering metrics and business forecasting. It makes it easy to estimate a 10K, 100K or 1M request month without rebuilding a spreadsheet.
How to read the result
The cost per 1,000 requests is an estimate at your selected token sizes. Use cost per request to assess one API call, then scale volume to forecast monthly traffic. Compare the selected model’s input and output rates before interpreting the total.
The math
Cost per 1,000 requests = 1,000 × (input tokens per request × input rate + output tokens per request × output rate) ÷ 1,000,000.
Pricing sources
Rates use the published TokenCOGS pricing registry and its September 16, 2026 fallback snapshot. Live values may differ; see methodology and source limitations before procurement.
Planning limitations
Actual invoices can differ because providers may apply long-context pricing, caching rules, batch rates, tool-call fees, regional processing, negotiated contracts, taxes, or future pricing changes. Treat the output as a planning estimate.
Use it to create scenarios and catch order-of-magnitude mistakes. Before committing spend, verify the selected model's live provider pricing and compare the estimate with actual usage telemetry.
Why can two models differ even with identical request counts?
Providers price input and generated output tokens differently. The split between input and output in your workload changes the relative costs.
Why use cost per 1,000 requests?
It gives teams a stable unit for comparing model choices and forecasting traffic scenarios while preserving the input/output token assumptions.