Subscription vs API cost calculator

The question no vendor will answer for you

Anthropic sells both a flat-rate subscription and metered API access, and has no reason to tell you which one is cheaper for your specific usage — the honest answer is "it depends," and a calculator that might point you toward the cheaper option isn't something a seller builds voluntarily. This tool exists to answer it with your numbers: how many messages you send per day, your typical input and output length, how much context gets re-sent on each turn, and how many days a month you're actually working.

What it computes

Enter your usage shape and the calculator prices the same workload two ways — metered API, per model, and against the subscription cost you tell it you're paying (this site does not publish subscription plan prices; see why below). It then names the break-even point in plain language: below roughly N messages a day, the API is cheaper; above it, the subscription is. It also lets you toggle the two levers that move the API side of that comparison the most: prompt caching and batch processing.

Why there's no subscription price on this page

You will not find a Claude Pro, Max or Team price published anywhere on this calculator, or anywhere else on this site. Consumer plan pricing is not published in any source we treat as authoritative, and it varies by region, currency, tax treatment and billing period — a number we typed in here six months ago would very likely be wrong for your account today. So instead of guessing, the calculator asks what you actually pay each billing period and computes from there. That makes the comparison correct for every reader, in every currency, regardless of how plan pricing changes in the future — which a hardcoded number never would be.

A worked, illustrative example

Take a support-bot integration sending roughly 500 messages a day across 22 working days a month — 11,000 messages monthly — where each message carries about 2,000 input tokens (mostly a repeated system prompt and retrieved context) and produces about 400 output tokens. Priced on Sonnet 5 at its standard metered rate with no caching, that's roughly 22M input tokens and 4.4M output tokens a month — about $66 of input and $66 of output, around $132/month metered, uncached.

Now suppose 1,800 of those 2,000 input tokens per message are the same stable prefix — the system prompt and shared context — and it stays warm in cache across the request volume. Prompt-cache reads bill at roughly a tenth of the standard input rate. Re-run the same math with that portion billed as a cache read instead of a fresh read and the input cost drops sharply while the output cost (never cacheable) stays the same — the same workload lands closer to $79/month, roughly a 40% reduction, before even considering batch processing for anything that isn't latency-sensitive.

That example is deliberately simplified — it prices cache reads only and doesn't model the occasional cache-write cost a real request pattern incurs, or the choice between the 5-minute and 1-hour cache TTL. The calculator above does both, using your actual request pattern rather than an assumed hit rate; see the prompt-caching savings calculator for that mechanic in isolation.

The levers, and where each pays off

Prompt caching pays off once a stable prefix is read more than once within its TTL — the math works out to roughly two requests within a 5-minute window, or three within a 1-hour window, before caching beats paying full price every time. Batching trades latency (results typically land within an hour, and always within 24) for roughly half the standard per-token price — a strong trade for anything that doesn't need a synchronous response, and a bad one for anything that does. Both levers are folded into this calculator's API-side total, so the comparison you get already reflects whichever combination is cheapest for the usage pattern you entered, not the naive metered price alone.

If you're also on the OpenAI side

Comparing the same workload against OpenAI's Codex ecosystem is a different sourcing problem entirely, since Anthropic and OpenAI publish neither the same figures nor the same mechanics — CodexHow is building the equivalent numbers for that side, so the two sites can eventually run one identical, described workload through both and put the two bills side by side.