Batch API Cost Estimator
Calculate potential savings using OpenAI's Batch API for high-volume tasks
Model Configuration
Cost Breakdown
Batch requests are processed within 24 hours. Ideal for non-urgent tasks.
Related Tools
Context Window Calculator
Calculate if your text fits within specific model context windows
Embedding Cost Calculator
Calculate the cost of generating embeddings for a dataset
Fine-Tuning Cost Calculator
Estimate the cost of fine-tuning models on your custom dataset
Image Generaton Cost Calc
Calculate costs for generating images with DALL-E 3, Midjourney, etc.
AI Token Counter
Count tokens and estimate API costs for GPT-4, Claude, Gemini, Llama and other AI models with real-time pricing
AI Cost Calculator
Calculate and compare API costs for GPT-4, Claude, Gemini and other AI models with batch and comparison modes
Complete Guide to OpenAI Batch API Costs
How to Use This Tool
The Batch API Cost Estimator helps you calculate potential savings by switching from standard synchronous API calls to OpenAI's asynchronous Batch API. Follow these steps:
Select Your Model
Choose the OpenAI model you intend to use (e.g., GPT-4o, GPT-4o mini, or GPT-3.5 Turbo). All listed models qualify for the 50% Batch API discount.
Enter Request Volume
Input the number of requests you plan to process. The Batch API is particularly beneficial for high-volume tasks like content classification, data extraction, or bulk translation.
Estimate Token Usage
Provide average input (prompt) and output (completion) token counts per request. You can use the Token Counter tool if you're unsure about typical lengths.
Review Savings
Check the calculated costs and potential savings. The tool automatically applies the 50% discount to show you the difference between standard and batch processing.
What is the Batch API?
The Batch API is a feature from OpenAI that allows you to send large groups of requests asynchronously. Instead of processing requests one by one in real-time and getting immediate responses, you submit a file containing up to 50,000 requests, and OpenAI processes them within a 24-hour window.
In exchange for the flexible timing, OpenAI offers a 50% discount on token costs compared to the standard synchronous API. This makes it ideal for background jobs where immediate latency is not critical.
When to Use Batch API
Ideal Use Cases
- Data Classification: Labeling large datasets for analysis.
- Content Generation: Creating thousands of SEO articles or product descriptions.
- Summarization: Processing backlog of documents or transcripts.
- Sentiment Analysis: Analyzing historical customer feedback.
- Bulk Translation: Translating large content libraries.
When Not to Use
- Customer Chatbots: Requires real-time responses.
- Interactive Tools: Users cannot wait 24 hours.
- Time-Sensitive Alerts: Immediate processing required.
- Small Batches: Setup overhead may not be worth it for few requests.
Price Comparison Table
| Model | Standard Input/M | Batch Input/M | Standard Output/M | Batch Output/M |
|---|---|---|---|---|
| GPT-4o | $2.50 | $1.25 | $10.00 | $5.00 |
| GPT-4o mini | $0.15 | $0.075 | $0.60 | $0.30 |
| GPT-3.5 Turbo | $0.50 | $0.25 | $1.50 | $0.75 |
Implementation Considerations
Key Technical Details
- File Format: Requires JSONL files with specific internal structure.
- Rate Limits: Batch API has separate, higher rate limits than standard API.
- Retrieval: You must poll for status or check periodically to download results.
- Error Handling: Errors in individual requests within a batch are reported in the results file.
Example Workflow Costing
Consider a project to classify 100,000 customer support tickets (avg 300 words each) using GPT-4o mini:
Standard API Approach
- Input Tokens: ~40M
- Output Tokens: ~5M
- Cost: $6.00 + $3.00
Batch API Approach
- Input Tokens: ~40M
- Output Tokens: ~5M
- Cost: $3.00 + $1.50