Calculator Inputs
Enter per-request values. The tool multiplies by request count and applies pricing, caching, and fixed fees.
Example Data Table
A small sample token log you can replicate with your own measurements.
| Request | Prompt | Overhead | Output | Cached | Total tokens |
|---|---|---|---|---|---|
| Summarize a report | 720 | 140 | 280 | 200 | 1,140 |
| RAG answer with citations | 980 | 320 | 420 | 650 | 1,720 |
| Tool call + structured output | 640 | 110 | 380 | 0 | 1,130 |
Formula Used
- Input_per_request = Prompt + Overhead + Tool + Embedding + Image
- Total_input = Input_per_request × Requests
- Total_output = Completion × Requests
- Total_tokens = Total_input + Total_output
- Billable_input = (Input_per_request − Cached) × Requests
- Cost_input = (Billable_input ÷ 1000) × Input_rate
- Cost_cached = (Cached_total ÷ 1000) × Input_rate × (1 − Discount)
- Cost_output = (Total_output ÷ 1000) × Output_rate
- Total_cost = Cost_input + Cost_cached + Cost_output + Fixed_fees
How to Use This Calculator
- Choose “Enter tokens directly” or “Estimate tokens from characters”.
- Enter per-request prompt, overhead, and completion values.
- Add tool, embedding, or image token equivalents as needed.
- Set pricing for input/output and any cache discount.
- Click Calculate to see totals, cost per request, and projections.
- Download CSV for spreadsheets or PDF for sharing.
Token Accounting That Matches Real Workloads
Token spend is not just prompt plus output. Production runs also include routing overhead, retrieval context, tool schemas, and optional embedding or vision inputs. This tracker separates those components per request, then scales them by request count. Use it to compare workflows like summarization, RAG, and tool‑augmented agents, and to spot where overhead is dominating useful tokens. Track input mix to prioritize prompt or pipeline fixes.
Cost Modeling With Transparent Rate Controls
Enter input and output rates per 1K tokens to mirror your provider pricing. The calculator converts billable tokens to cost, adds any fixed fee per request, and reports total cost and cost per request. This makes it easy to benchmark changes such as shorter completions, tighter prompts, or cheaper routing, using the same rate card across experiments. Run sensitivity checks by adjusting rates and rerunning token profiles.
Caching Impact and Savings Estimation
If part of your input repeats, caching can reduce spend. Provide cached tokens per request and an optional cache discount percentage. The tracker prices cached tokens at the discounted rate, computes the baseline cost at full price, and shows savings. This is useful for repetitive system instructions, shared context blocks, and prompt templates reused across users. Even a 30% discount compounds at millions of tokens.
Capacity, Throughput, and Context Risk
Operations teams care about speed and limits. By adding average latency per request, the tool estimates tokens per second using average tokens per request divided by seconds of latency. The context limit field adds guardrails: you’ll see warnings when per‑request tokens approach or exceed the configured limit, helping prevent truncation or failures in long conversations. Pair throughput with concurrency targets to size workers and queues.
Budget Forecasting and Daily Projections
Planning requires forecasts, not just per‑request math. Add a daily request volume to project daily and 30‑day spend from the computed cost per request. If you also enter a budget, the calculator estimates how many requests you can afford before crossing the cap. Use these projections to set alerts, tune defaults, and allocate capacity by team or feature. Export results for spend reviews and optimization roadmaps.
FAQs
1) What counts as overhead tokens?
Overhead covers extra context beyond your prompt, such as system wrappers, retrieval passages, routing hints, tool schemas, and safety buffers. Measure it from logs, or estimate based on typical pipeline additions.
2) How do cached tokens affect billing?
Cached tokens are priced using your input rate plus the cache discount. The tool also shows savings versus paying full price for those same tokens, so you can quantify the benefit.
3) Should I enter embedding tokens here?
Yes, if you tokenize text for embeddings per request and want an end‑to‑end token picture. Add only the tokens you attribute to each request, not one‑time indexing jobs.
4) Why is the tokens‑per‑second estimate low?
TPS is computed from average tokens per request divided by latency seconds. High network latency, longer completions, or heavy tool output will reduce TPS. Use real latency averages for better accuracy.
5) How accurate is character‑based estimation?
It is a rough planning shortcut. Tokenizers vary by language, whitespace, and formatting, so the same character count can map to different token counts. Calibrate chars‑per‑token using a few real samples from your logs.
6) How can I reduce cost per request quickly?
Start by trimming completion length, removing repeated context, and caching stable instructions. Next, reduce overhead from retrieval and tool schemas, and verify you are not over‑allocating context windows. Re‑run the calculator after each change to compare results.