Calculate Batch Processing Cost for a Dataset

Processing large datasets through AI APIs requires careful cost estimation before committing to a run. The batch APIs from OpenAI and Anthropic offer 50% discounts versus real-time pricing but have 24-hour completion windows — making them ideal for offline data processing tasks but unsuitable for user-facing features. This example shows a 10,000-row dataset and calculates total cost, estimated processing time, and per-item cost across real-time and batch modes for several models. The key variables in batch cost estimation are: average tokens per item (input + output), the model being used, whether you qualify for batch API pricing, and whether you have existing cached prompts that share a common prefix. Prompt caching (available on GPT-4o and Claude models) reduces input token costs by 50-90% for requests that share a long common prefix like a system prompt or reference document — this is the single most impactful cost optimization for large-scale batch jobs. For estimating token counts before running the full batch, sample 100 representative items from your dataset, compute average token counts, and add 15% margin. Real datasets always have outliers with much higher token counts than average — capping max_tokens and handling truncated responses gracefully prevents individual long items from distorting your cost estimate.

Dataset: product_reviews.csv
Total rows: 10000
Task: sentiment analysis and category extraction
Average input tokens per row: 350
Expected output tokens per row: 80
Model options: gpt-4o, gpt-4o-mini, claude-3-5-haiku
Use batch API: yes
Prompt caching eligible: yes (shared 500-token system prompt)