AI Token Cost Calculator
To estimate the cost of an LLM API call, choose a model, enter input and output token counts, and optionally set a repeat workload (requests per day or per month). The calculator returns per-request cost, input cost, output cost, cached-prompt cost and savings, plus daily and monthly projections — works as an OpenAI token calculator, Claude token calculator, or general LLM cost calculator for ChatGPT-style API workflows.
How this AI token cost calculator works
The first release stays simple on purpose: you bring the token counts, the page applies the selected model's local pricing table, and the result scales to a daily or monthly workload when needed. In practice, that makes it useful as an OpenAI token calculator, Claude token calculator, or general token cost estimator.
Notes and boundaries
AI token cost calculator FAQ
- Can I use this page as an OpenAI token calculator?
- Yes. If you choose a supported OpenAI model, the page estimates prompt, completion, and cached-input cost from the model's current pricing data, then scales the result by request volume when repeated mode is enabled.
- Can I estimate Claude token cost here?
- Yes. Choose a supported Anthropic Claude model and enter the input, output, and optional cached-input tokens you expect per request. The calculator then estimates per-request, daily, or monthly token cost.
- Does this work as a ChatGPT token calculator?
- For API usage, yes. If you are pricing OpenAI API models used in a ChatGPT-like product flow, this page works as a ChatGPT token calculator. It is not meant to estimate ChatGPT Plus, Pro, Team, or Enterprise subscription fees.
- What is the difference between an OpenAI token calculator and a token cost calculator?
- An OpenAI token calculator usually refers to pricing one OpenAI model. A token cost calculator is broader: it applies the same token-based math across supported providers such as OpenAI, Claude, and Gemini.
- What are cached tokens?
- Cached tokens are prompt tokens billed at a discounted cached-input rate when the provider supports prompt caching or an equivalent cache mechanism.
- Why might this estimate differ from my actual bill?
- Real bills can differ because of tokenizer differences, retries, tool calls, grounding, long-context tiers, regional processing, provider-side pricing changes, and differences between aggregated pricing data and the provider's final billing logic.
- Which providers and models are included?
- The page currently focuses on OpenAI, Anthropic Claude, and Google Gemini, and aims to cover a broader set of their priced text and reasoning models while still excluding categories like image, audio, embedding, and other non-comparable SKUs.
Related AI planning tools
These tools sit next to LLM cost math in the planned AI cluster and help users move from pricing to comparison and token planning.