Batch Workload Cost Estimator (OpenAI)
OpenAI Batch API Cost Calculator estimates batch input, cached-input, and output token costs using current model pricing, so you can plan API spend in dollars.
Result
No estimate yetLocal math: workload totals via LLMTokenCore.batchWorkload, prices via the versioned catalog. How was this calculated? Totals = N × averages; cost = catalog rate × tier multiplier.
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OpenAI Batch Api Cost Calculator
TL;DR Summary
The OpenAI Batch Api Cost Calculator estimates the token cost of an OpenAI Batch API workload from your selected model, total input tokens, cached input tokens, and output tokens. It is a planning estimate based on documented model pricing, and privacy behavior for the page is not specified, so avoid entering sensitive information unless the page clearly explains how submitted data is handled.
About This Tool
The OpenAI Batch Api Cost Calculator is designed to help developers, AI engineers, product teams, and API users estimate what a batch workload may cost before they submit it to OpenAI. Instead of doing several token-price calculations by hand, you can select a model, enter your expected token usage, and get an estimated dollar total.
The calculator focuses on token-based Batch API costs. OpenAI's Batch API is intended for asynchronous groups of requests that do not require an immediate response. OpenAI documents Batch API pricing at a discount compared with synchronous API pricing, while the exact amount you pay depends on the model and the number and type of tokens used.
To use the calculator, select the OpenAI model that matches your planned workload. Then enter the total number of input tokens and the total number of output tokens. If part of the input is eligible for the model's cached-input price, enter that amount in the cached input field. The calculator separates uncached input from cached input so the two portions can use their respective rates.
For example, if a batch contains 1,000,000 total input tokens and 250,000 of those tokens are cached, the calculator treats 750,000 tokens as regular input and 250,000 as cached input. It then applies the selected model's Batch input rate to the uncached portion and its cached-input rate to the cached portion. Output tokens are calculated separately.
The tool supports several currently documented OpenAI models, including GPT-6 Astra, GPT-6 Sol, GPT-6 Luna, GPT-5.5, GPT-5.4, GPT-5.4 Mini, GPT-5.4 Nano, GPT-5.2, GPT-5, GPT-5 Mini, GPT-5 Nano, o3, and o1. Model availability and pricing can change, so the displayed calculation should be treated as a planning estimate tied to the pricing data represented by this calculator.
For selected models with documented long-context pricing, the calculator also provides a context-pricing option. The long-context option is relevant when input-token usage crosses the documented long-context threshold. For models without a separate long-context calculation in this tool, the calculator uses the standard Batch pricing represented for that model.
Who Can Use It?
This tool can be useful for developers estimating API budgets, teams planning large-scale classification or generation jobs, researchers preparing batch experiments, and businesses comparing expected token usage across supported models. It can also help when you want to understand how a change in model choice or output size may affect an expected API bill.
The calculator is especially useful when you already have an estimate for the number of tokens your batch will process. A token is a unit used to measure text processed by an AI model. Token counts are not always the same as word counts, so the most useful inputs are token counts from your actual workload, tokenizer, API usage data, or a representative sample.
Inputs
- OpenAI Model: Select the model whose Batch pricing you want to estimate.
- Context Pricing: For supported models, choose standard or long-context pricing.
- Total Input Tokens: Enter the total input tokens expected across the batch.
- Cached Input Tokens: Enter the portion of input tokens expected to receive the model's cached-input rate.
- Total Output Tokens: Enter the output tokens expected across the batch.
Cached input must not exceed total input tokens. If you do not expect any cached input, use zero. The calculator does not estimate token counts from raw text; it expects token counts as inputs.
Outputs
The main result is the estimated Batch API cost in U.S. dollars. The calculator also breaks the estimate into uncached input cost, cached input cost, and output cost. This makes it easier to see which part of the workload contributes most to the total.
The result is an estimate rather than an invoice. Actual API charges can depend on the model, pricing tier, request characteristics, eligible pricing rules, and changes made by OpenAI after the pricing information represented by the calculator was published or updated.
How to Use
- Step 1: Select the OpenAI model you plan to use for the Batch API workload.
- Step 2: If the selected model supports the calculator's long-context option, choose the context pricing that matches your expected input-token usage.
- Step 3: Enter the total input tokens expected across your batch.
- Step 4: Enter the number of cached input tokens if you expect part of the workload to use cached-input pricing; otherwise enter zero.
- Step 5: Enter the total output tokens you expect the batch to generate.
- Step 6: Review the estimated total and the separate input, cached-input, and output costs.
- Step 7: Compare the estimate with your own workload assumptions and the current OpenAI pricing documentation before committing to a production budget.
Technical Explanation and Formula
The calculator uses the standard token-pricing approach represented by the selected model's Batch rates. The calculation separates regular input tokens from cached input tokens.
Uncached Input Tokens = Total Input Tokens − Cached Input Tokens
Input Cost = (Uncached Input Tokens ÷ 1,000,000) × Input Price per 1M Tokens
Cached Input Cost = (Cached Input Tokens ÷ 1,000,000) × Cached Input Price per 1M Tokens
Output Cost = (Output Tokens ÷ 1,000,000) × Output Price per 1M Tokens
Total Estimated Cost = Input Cost + Cached Input Cost + Output Cost
All token quantities are measured in tokens. Prices are measured in U.S. dollars per 1 million tokens. The calculator keeps the intermediate calculations unrounded and rounds the displayed dollar results to six decimal places.
For supported long-context models, the calculator applies the documented long-context pricing multipliers represented in the current pricing data. The calculator does not attempt to estimate unrelated charges such as separate tool-call fees, storage charges, or other products unless those costs are represented by the selected token rates.
Worked Example
Suppose a workload uses GPT-6 Luna with 1,000,000 total input tokens, 250,000 cached input tokens, and 500,000 output tokens under standard Batch pricing.
Using the documented standard Batch rates represented in the calculator, the uncached portion is 750,000 tokens. The input portion therefore costs 750,000 ÷ 1,000,000 × $0.10, or $0.075. The cached portion costs 250,000 ÷ 1,000,000 × $0.01, or $0.0025. The output portion costs 500,000 ÷ 1,000,000 × $0.50, or $0.25.
The estimated total is therefore $0.3275.
Quick Reference
| Model | Batch Input / 1M | Cached Input / 1M | Output / 1M |
|---|---|---|---|
| GPT-6 Astra | $10.00 | $1.00 | $50.00 |
| GPT-6 Sol | $2.00 | $0.20 | $10.00 |
| GPT-6 Luna | $0.10 | $0.01 | $0.50 |
| GPT-5.5 | $5.00 | $0.50 | $30.00 |
| GPT-5.4 | $2.50 | $0.25 | $15.00 |
| GPT-5.4 Mini | $0.75 | $0.075 | $4.50 |
| GPT-5.4 Nano | $0.20 | $0.02 | $1.25 |
| GPT-5.2 | $1.75 | $0.175 | $14.00 |
| GPT-5 | $1.25 | $0.125 | $10.00 |
| GPT-5 Mini | $0.25 | $0.025 | $2.00 |
| GPT-5 Nano | $0.05 | $0.005 | $0.40 |
| o3 | $2.00 | $0.50 | $8.00 |
| o1 | $15.00 | $7.50 | $60.00 |
These rates are the pricing values represented by the calculator and should be checked against OpenAI's current pricing page before using the estimate for a production budget.
Why Use This OpenAI Batch Api Cost Calculator & How Our Calculator Beats the Competition
The practical difference between this calculator and other cost-estimation methods is how much manual work is required. The calculator applies the selected token rates to the values you enter and separates the resulting cost components. A spreadsheet can provide similar flexibility, while manual calculation gives you full control but requires you to maintain the formulas yourself.
| Method | Ease of Use | Calculation Speed | Best For | Limitations |
|---|---|---|---|---|
| Toolhox Calculator | Enter model and token totals | Immediate calculation | Quick Batch API cost estimates | Depends on entered token counts and represented pricing data |
| Manual Calculation | Requires arithmetic | Depends on the user | Checking a simple calculation | Easy to use the wrong rate or miss a token category |
| Spreadsheet | Requires setup | Fast after setup | Custom budgeting and repeated scenarios | Requires formulas and pricing data to be maintained |
| Professional Cost or Usage Software | Varies by product | Varies by product | Broader operational or financial analysis | May include features beyond a simple token-cost estimate |
Assumptions and Limitations
This calculator assumes that the token counts you enter are representative of the workload you want to price. It does not tokenize raw prompts for you, predict future output length, or inspect your OpenAI usage history.
The calculation also assumes that the selected model and pricing category match your actual API workload. Model pricing can change. Model availability can change. Some OpenAI services can have additional pricing rules that are not part of a basic input-and-output token estimate.
Long-context pricing is represented only for the models and threshold behavior included in this calculator. The calculator is not a complete reproduction of OpenAI's billing system.
Cached input is also dependent on whether your workload actually qualifies for cached-input pricing. Entering a cached token value does not create caching eligibility; it only changes the estimate for the portion you identify as cached.
Use the result as a budgeting and planning estimate, not as an invoice. For production financial planning, compare the estimate with the current OpenAI pricing documentation and your actual API usage records.
OpenAI documents that Batch API jobs are asynchronous and are designed for workloads that do not require immediate responses. OpenAI also documents a 24-hour completion window for Batch API processing. Those operational characteristics are separate from the token-cost calculation performed here.
