AI MODEL AGGREGATION / SMALL-TEAM GUIDES
Estimate LLM API cost before scaling a feature
Plan input, output, retries and workload size, with explicit boundaries around token-based estimates.
The short answer
For ordinary token-based pricing, estimate input tokens times the input rate plus output tokens times the output rate, using the rate's stated unit. Use current prices for the selected model and account group. This does not cover every billing mode or produce a guaranteed final charge.
Build a workload sheet
Record requests per user action, expected actions, input tokens per request and output tokens per request. Include fixed system instructions and conversation history in the input estimate. If an action performs generation followed by a revision pass, count both calls. Treat an estimate as a range until representative test runs show how long the actual outputs are.
Use matching units
When prices are quoted per million tokens, divide each token total by 1,000,000 before multiplying. An illustrative workload of 200,000 input tokens at $1 per million and 50,000 output tokens at $4 per million totals $0.40. The rates in this example are fictional arithmetic inputs, not current TokenBison prices. Copy live model rates before using the calculation for a decision.
State what the estimate excludes
Check whether cache, image, audio, per-request, tiered or account-group pricing applies. Do not flatten those modes into a generic text-token estimate. Keep retries and rejected outputs in the workload totals and compare estimated usage with actual records after a small run. If they differ, inspect the unit, model, group and repeated requests before expanding traffic.
Evidence and boundaries
These are documentation references and proposed evaluation workflows, not measured model rankings or a guarantee of savings. Check the current catalog and policies before use.
TokenBison current pricing · reviewed 2026-09-16
The pricing implementation distinguishes input and output token costs, model billing modes and account groups. This guide gives an evaluation method, not a current price quote or measured savings.
Put the guide into practice
Review current model prices, then make a small API request with public or non-sensitive sample data. Compare the output against your own acceptance criteria.