LLM Token Cost Calculator

This LLM token cost calculator helps you estimate how much you will pay your AI provider based on the number of tokens you send and receive and the price you pay per 1,000 tokens. It is designed for engineers, founders, analysts, and finance teams who need a quick way to turn projected token usage into an approximate dollar cost for budgeting and planning.

How token-based pricing works

Most large language model (LLM) APIs bill by tokens instead of characters or raw requests. A token is a small chunk of text – often a few characters or part of a word. When you send a prompt to a model, the provider converts your text into tokens, performs inference, and then returns output tokens that are also counted for billing.

Providers usually publish prices as a dollar amount per 1,000 tokens (sometimes written as “per 1K tokens”). For example, you might see a rate like $0.002 per 1,000 tokens for a smaller model or $0.02 per 1,000 tokens for a larger, more capable model. The calculator on this page converts your projected token usage and your per-1K price into an estimated total cost.

In its simplest form, token-based pricing treats cost as directly proportional to the number of tokens processed. Double the tokens and, assuming the same unit price, you double the bill. This proportional relationship is what the calculator turns into a concrete dollar estimate.

The cost formula used by this calculator

The calculator assumes a single blended rate per 1,000 tokens. Let:

• T be the total number of tokens you expect to process (input and output combined, if you choose), and
• P be the price in dollars per 1,000 tokens.

The cost is then calculated using a simple proportional formula:

In plain language, you divide your token count by 1,000 to get how many “blocks” of 1,000 tokens you are using, then multiply by the price per block. The calculator applies this formula and typically rounds the result to two decimal places so you see a friendly dollar amount.

If you know that your provider charges different prices for input and output tokens, you can still use the same formula by computing a blended effective price. For example, if you expect 60% of tokens at one rate and 40% at another, you can combine them into a single per-1K figure and enter that into the price field.

Interpreting the estimated cost

The calculator does not decide whether your token input is per request, per user, per day, or per month. Instead, it treats your token number as a single total and tells you how much that total would cost at your specified price per 1,000 tokens.

To use the result appropriately:

• If you enter the expected tokens for a single request, the result will be the cost per request.
• If you enter the tokens you expect to use in a day, the result will represent your daily cost.
• If you enter projected tokens for a month, the result will be your monthly estimate.

You can move between timeframes by scaling the token input. For example, if you know the cost per request, you can multiply the tokens per request by your expected number of requests per day or per month and run the calculation again. This flexibility lets you align the calculator output with the budgeting horizon that matters for your project or organization.

Worked example: small project estimate

Imagine you are building a small internal tool that answers questions about your documentation. You expect relatively light usage, and you choose a model that costs $0.002 per 1,000 tokens. You estimate that, across all users, the system will process about 50,000 tokens per month (including both prompts and responses).

In this situation:

• T = 50,000 tokens
• P = 0.002 dollars per 1,000 tokens

Apply the formula:

First, divide tokens by 1,000:

50,000 ÷ 1,000 = 50 blocks of 1,000 tokens.

Then multiply by the per-1K price:

50 × 0.002 = 0.1

The estimated monthly cost is $0.10 at this usage level and price. This shows that for small-scale or prototype applications, token costs may be negligible, especially on lower-priced models.

Worked example: production-scale application

Now consider a production customer-support chatbot that handles many requests each day. Suppose you estimate:

• Average tokens per conversation (input + output): 2,000
• Average conversations per day: 5,000
• Model price: $0.01 per 1,000 tokens

First compute your total tokens per day:

2,000 tokens/conversation × 5,000 conversations/day = 10,000,000 tokens per day.

So you have:

• T = 10,000,000 tokens
• P = 0.01 dollars per 1,000 tokens

Apply the same formula:

10,000,000 ÷ 1,000 = 10,000 blocks of 1,000 tokens.

10,000 × 0.01 = 100

The estimated daily cost is $100. To get a rough monthly figure, multiply by the number of days in your billing period. For a 30-day month, the estimate would be $3,000.

This example illustrates how the same simple formula can scale from hobby use all the way to substantial production workloads simply by changing the token input and unit price.

Comparing different pricing tiers

LLM providers often offer multiple models with different capabilities, latency, and prices. The table below shows how the same token usage can lead to different costs depending on which pricing tier you choose.

For each row, the same formula is used. Changing the price per 1,000 tokens or the total token volume can have a large impact on overall spend. The calculator lets you plug in your own values to compare alternative models and usage levels before you commit.

Assumptions and limitations of the calculator

This tool is designed for quick estimates, not exact billing predictions. It relies on several simplifying assumptions:

• Single blended price. The calculator assumes you enter one price per 1,000 tokens that applies to all tokens. Many providers charge different rates for input and output, or for different model features. To use the calculator, you can combine those into a single effective rate, but that is still an approximation.
• No automatic split between input and output. The token field is a total. If your provider bills input and output separately, you can either sum them yourself or, for a rough estimate, double an approximate input count if you expect inputs and outputs to be similar in size.
• No tiers, minimums, or discounts. Some vendors apply tiered pricing (e.g., cheaper once you exceed a certain volume), minimum monthly charges, or volume discounts. The calculator does not model those rules; it uses a straight linear rate.
• Taxes and fees excluded. Any applicable taxes, regulatory fees, or currency conversion effects are not included in the calculation.
• Rounding differences. Providers may round tokens and monetary amounts differently from this tool. As a result, the output here may differ slightly from your final invoice, even with the same inputs.

For accurate billing, always confirm details on your provider’s official pricing page and compare your estimates with actual usage data from their dashboard once your system is live.

Practical budgeting tips

Because token costs scale with usage, small implementation choices can influence your monthly bill. A few practical approaches include:

• Track tokens per request. Measure the average tokens consumed per interaction. Many SDKs or dashboards expose token counts directly. Multiply that by your expected request volume to estimate monthly usage.
• Optimize prompts. Shorter, more focused prompts reduce tokens without sacrificing quality. Remove redundant instructions, boilerplate, or unused context where possible.
• Limit maximum response length. Configure your model’s maximum output tokens to a reasonable upper bound so that occasional long replies do not drive unexpected costs.
• Use smaller models where acceptable. For classification, routing, or simple transformations, a cheaper model may be sufficient. Reserve more expensive models for tasks where they clearly add value.
• Cache frequent answers. If many users ask similar questions, caching or storing canonical answers can reduce repeated calls and the associated token spend.

You can use the calculator iteratively while exploring these tactics: adjust the expected token count, keep the same price per 1,000 tokens, and see how each optimization might affect your projected spend.

Frequently asked questions

How can I approximate tokens from word counts?

As a rough rule of thumb, many English-language texts have about 0.75 tokens per word to 1 token per word, depending on punctuation, formatting, and the tokenizer used by your provider. If you know how many words are in a typical prompt or document, you can multiply the word count by about 0.75 to estimate tokens, then use that in the calculator.

What if my provider bills input and output tokens differently?

If your provider has separate prices for input and output tokens, you can either run two separate calculations (one for input, one for output) and sum the results, or compute a blended rate. For a blended rate, multiply the expected input tokens by the input price, do the same for output tokens with the output price, add the two costs together, and divide by the total number of tokens. The result is an effective price per token that you can convert to a price per 1,000 tokens for this tool.

How do I estimate cost for mixed workloads (chat + embeddings, etc.)?

Different API features, such as chat completions, embeddings, and fine-tuning, may each have their own pricing. To use this calculator, you can:

1. Estimate tokens and price for each workload type separately.
2. Use the calculator for each type to get an individual cost estimate.
3. Add the resulting dollar amounts to approximate your total monthly bill.

This keeps the simple linear formula while acknowledging that mixed workloads often span multiple endpoints or models.

How often should I revisit my estimates?

Provider prices and your own usage patterns can change over time. It is a good practice to revisit your estimates whenever you change models, add major features, or see a noticeable shift in traffic. Periodic checks against your provider’s billing dashboard can help you refine the token and price inputs you use here so your projections stay realistic.

Using the calculator for planning

To plan your LLM budget with this tool, decide on the timeframe you care about (per request, daily, weekly, or monthly), estimate the total tokens for that period, look up your provider’s current price per 1,000 tokens, and enter both numbers. The resulting estimate gives you a quick sense of whether a particular model and usage level will fit within your constraints.

Because the underlying formula is simple and transparent, you can easily adjust assumptions and run several scenarios in a row. This makes the calculator a practical companion for model selection, capacity planning, and discussions between technical teams and stakeholders about the cost of deploying AI features.