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About this tool
Project Claude API costs by model, request count and token size, with prompt-cache read/write pricing and the batch discount.
This calculator projects Anthropic Claude API spend from requests per day and average input and output tokens per request, priced at each model's per-1M-token rates. It models Claude's full billing surface: prompt-cache reads at roughly 0.1x the input rate, cache writes at a 1.25x (5-minute TTL) or 2x (1-hour TTL) premium, and the Message Batches API's 50% discount — with every rate editable so estimates track the live pricing page.
Open Claude API Cost Calculator on AltFTool — it loads instantly in your browser.
Enter the values you already know.
Fine-tune the options to match your scenario.
Read the result and use it in your planning or reporting.
Separate input and output rates with cache reads at 0.1x and TTL-dependent write premiums.
Shows the monthly saving your cache-read and cache-write mix produces versus running uncached.
Loads published Opus, Sonnet and Haiku rates and lets you override any figure when prices change.
On Anthropic's first-party API, Opus-tier models (Claude Opus 5, Opus 4.8) list at $5 per million input tokens and $25 per million output tokens, Sonnet-tier models at $3/$15, and Claude Haiku 4.5 at $1/$5. Prices change with model releases and differ on Amazon Bedrock and Vertex AI, so confirm against Anthropic's pricing page before budgeting.
Cache reads bill at about 0.1x the base input rate, while cache writes carry a premium — 1.25x for the default 5-minute TTL and 2x for the 1-hour TTL. With the 5-minute TTL a cached prefix pays for itself from the second request (1.25x + 0.1x is cheaper than 2x uncached), so workloads with a stable system prompt and steady traffic save the most.
50% off both input and output tokens. Batches process asynchronously — most complete within an hour, with a 24-hour maximum — which suits evaluations, backfills and other jobs that do not need an immediate response.
Thinking tokens bill as ordinary output tokens at the model's output rate — there is no separate thinking price. Because output typically costs five times the input rate, workloads that trigger deep reasoning can see output become the dominant cost line; the effort setting is the main lever for controlling it.
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