How is the cost delta calculated?

Each model prices a request as (input tokens × input rate) + (output tokens × output rate), divided by a million. Both models are priced against the same workload and multiplied by your monthly volume; the delta is the difference.

Newer models are usually cheaper — is that always true?

Often, but not always. GPT-4o is much cheaper than the original GPT-4, and Claude 3.5 Sonnet undercuts Claude 2. But a jump to a top-tier model like Claude 3 Opus can cost more than the version it replaces. Always price your specific token mix.

Why does the token mix matter?

Input and output are priced separately, and the ratio changes the result. A workload that is output-heavy is more sensitive to the output price, so two models can rank differently depending on whether your requests are long-prompt or long-completion.

How should I weigh quality against cost?

If the new model is both cheaper and better it is a strict win. If it costs more, the quality field helps you frame the decision: a large accuracy gain that reduces downstream retries or human review can justify a higher per-call price.

Is my data sent anywhere?

No. The calculator runs entirely in your browser. Nothing you enter is uploaded, stored or logged.

API Version Upgrade Cost Impact Calculator

API version upgrade cost impact calculator

A new model generation lands — GPT-4o replacing GPT-4, Claude 3.5 Sonnet replacing Claude 2 — and the first question is always: what does this do to my bill? This tool prices your exact workload under both the old and new model so you see the monthly cost delta before you migrate, and frames it against the expected quality gain.

How it works

Each model bills input and output tokens at separate per-million rates. For one request:

cost = (input_tokens / 1e6) × input_price + (output_tokens / 1e6) × output_price

The tool computes that under both models, multiplies by your monthly volume, and reports the difference. A negative delta means the upgrade is cheaper; a positive delta means it costs more. The quality field lets you reason about the trade-off when the new model is pricier.

Tips and notes

Upgrades within a family usually cut cost dramatically — GPT-4o input is a fraction of original GPT-4 — but moving up a tier (to an Opus-class model) can increase spend, so always price your real token mix rather than assuming “newer is cheaper.” Watch the input/output ratio: output-heavy workloads are far more sensitive to the output price, which can flip the ranking between two candidates. And remember the indirect savings: a more capable model often needs fewer retries, less prompt scaffolding and less human review, so the true cost change can be better than the raw per-token delta suggests. Roll out gradually and measure quality on your own evals before committing the whole workload.