Batch API Cost Calculator (50% off bulk LLM jobs)
Compare real-time vs Batch API cost for a bulk job. Enter request count and per-request tokens to see how much the ~50% batch discount saves across models that support it. 100% local.
Last updated:
CommentsEnter your request count and per-request tokens to compare real-time vs Batch API cost — and see how much the ~50% batch discount saves on a bulk job. Runs entirely in your browser.
Real-time API cost
$375.00
Batch API cost
$375.00
You save with Batch
$0.00 (0.0%)
The Batch API processes requests asynchronously (typically within 24h) at a discount — usually ~50%. Use it for embeddings, evals, and bulk generation that isn't latency-sensitive. All math runs in your browser.
What is Batch API Cost Calculator?
For work that isn't latency-sensitive — embeddings, offline evaluations, bulk classification, data enrichment — the asynchronous Batch API is the single easiest way to cut cost, typically ~50% off both input and output tokens in exchange for results within a target window (often 24 hours). The catch is estimating whether the savings on your specific job justify the added complexity. This calculator multiplies your request volume by per-request tokens and applies each model's published batch rate, showing real-time cost, batch cost, and the dollar savings side by side.
How to estimate batch savings
- 1Pick a model.
- 2Enter the number of requests and the input / output tokens per request.
- 3Read the real-time vs batch cost and the amount saved.
Key Concepts
- Batch API
- Asynchronous bulk processing at a discount, with a delayed delivery window.
- Embedding jobs
- A classic batch workload — estimate token cost with the embedding calculator.
Frequently Asked Questions
How much does the Batch API save?
OpenAI and Anthropic offer roughly a 50% discount on both input and output tokens for their asynchronous Batch APIs, which return results within a target window (often 24h).
When should I use batch instead of real-time?
For anything not latency-sensitive: embeddings, offline evals, bulk classification, data enrichment, and large one-off generation jobs. Real-time is only worth the premium for interactive use.
Why does one model show no batch savings?
If a model has no published batch rate in our data, the batch figure falls back to the real-time price so you aren't misled.
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