OpenAI API Cost Calculator

What this calculator is for

Compare verified OpenAI options using exactly the same workload instead of headline rates in isolation.

OpenAI models can have different input, cached-input and output rates, so workload shape matters as much as the model name.

The calculator separates verified provider prices from your own workload assumptions. Published rates are evidence; traffic, customer behavior, token length, cache hits, agent retries, infrastructure and business overhead are scenario inputs. Keeping those two categories separate makes the estimate easier to audit and update.

How to build a useful estimate

Measure workload first. Start with requests per day, input tokens, output tokens, active users and days per month. For agents, count repeated calls, retries, subagents and tool loops instead of assuming every task is one model request.

Apply the right price category. Keep normal input, cached input, output and eligible batch processing separate. Do not apply a cache discount to traffic that is not actually eligible, and do not treat asynchronous batch rates as appropriate for latency-sensitive interactive work.

Stress-test the result. Run a normal case and a high-usage case. Increase traffic, output length or agent multiplier and identify which variable threatens the budget first.

Worked token-cost example

This page uses GPT-5.6 Luna from the verified OpenAI pricing data as a transparent example. The recorded rates are $0.2 per 1M input tokens, $0.02 per 1M cached-input tokens and $1.2 per 1M output tokens. A monthly workload with 5 million regular input tokens and 1.5 million output tokens has a simplified token component of about $2.8.

If half of the input were genuinely eligible for the recorded cached-input rate, the same simplified example becomes about $2.35. That is not a promised saving; it shows why cache eligibility must be measured. Taxes, tools, storage, retrieval, image or audio processing, regional premiums, commitments and negotiated terms may add costs outside this example.

From technical cost to a business decision

Choose cost only after separately validating capability, latency, reliability, safety and tool behavior for your task.

For a commercial AI product, convert model spend into cost per paying customer or transaction. Add payment fees and operating costs that exist even when model usage is zero, then compare the result with revenue and target margin. For internal AI, compare estimated cost with measured business value, keeping productivity claims separate unless you have evidence.

Do not choose a model solely because it is cheaper here. Capability, safety, privacy, latency, reliability, context limits, tool support, regional availability, support and contractual terms can matter more than a token-price difference. AI Cost Lab treats cost as one decision dimension rather than a quality ranking.

Cost controls worth testing

Reduce unnecessary context before changing models. Cap output where verbosity does not create user value. Reuse stable prompt prefixes when caching is supported and measured. Batch offline work when latency permits. Put limits around retries and agent loops. Route simple workloads to lower-cost verified models only after testing that the workload remains acceptable.

Track daily burn rate after launch. Monthly estimates are useful for planning, but a runaway loop or traffic spike can consume a budget long before the billing period ends. Budget alerts, request limits and workload telemetry are operational controls, not merely finance reporting.

Frequently asked questions