DevKits

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:

Comments

Enter 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%)

Claude 3 Haiku has no published Batch API discount in our data, so the batch figure falls back to the real-time price.

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

  1. 1Pick a model.
  2. 2Enter the number of requests and the input / output tokens per request.
  3. 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.

Try Next

LLM JSON Extractor

Extract every JSON object or array buried inside an LLM reply — even when it's wrapped in markdown fences, mixed with explanation text, or has trailing commas and single quotes. Auto-repairs each block. 100% local.

Related Tools

Reference & Guides