Batch API Savings Calculator

Batch API cuts LLM costs by ~50% for offline workloads. Calculate your savings from switching document processing, classification, or evaluation jobs to batch mode.

Batch API Calculator
Volume & Tokens
Model
Real-time cost
$975.00
Batch API cost
$487.50
You save
$487.50
Savings rate
~50%
Batch API qualifies if your 100,000 requests don't need real-time results. Results within 24h. No rate limit consumption.

Prices verified 2026-08-20 · Full prompt cost calculator

How to Use

Check if your workload qualifies

Batch API is for offline, non-real-time workloads: document classification, data extraction, content generation, evaluation runs. Results arrive within 24 hours — not suitable for user-facing features.

Enter your volume

Total number of requests, average input tokens, and average output tokens. These drive both real-time and batch costs.

Choose your model

Select your model. OpenAI and Anthropic both offer batch variants at ~50% discount. Google Gemini also offers batch processing.

Confirm the savings

Batch API saves exactly 50% in most cases. The only question is whether your workload can tolerate up to 24-hour completion time.

Batch API: the easiest 50% cost cut

If your workload produces results used within 24 hours rather than immediately, Batch API is the easiest cost reduction available. OpenAI, Anthropic, and Google all offer ~50% discounts for batch processing. For document processing, nightly evaluation runs, or content generation pipelines, batch API is almost always the right choice.

How to Cut This Cost

~50%

Switch to Batch API for any workload where results are used within 24 hours rather than immediately. Nearly every provider offers this discount.

Up to 30%

Combine Batch API with prompt caching on the static prefix (system prompt + instructions) for stacked savings.

Up to 80%

Route the simplest batch tasks to a cheaper model (GPT-5 mini / Gemini Flash). Classification and extraction rarely need a frontier model.

FAQ

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Last verified: 2026-08-20 · methodology · data sources