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LLM Batch API: Save 50% on Asynchronous Workloads

Learn how batch APIs from OpenAI, Anthropic, and Google can cut your LLM costs in half. Understand when batch processing makes sense and how to implement it.

Updated: 2026-04-06

TL;DR

Both OpenAI and Anthropic offer batch APIs with 50% discounts on all models. If your workload can tolerate hours-long response times instead of seconds, batch processing is the easiest way to halve your LLM costs.

Batch API Pricing: 50% Off Everything

The batch API concept is simple: you trade speed for savings. Instead of getting responses in seconds, you submit a batch of requests and receive results within hours (typically 1-24 hours). Both major providers offer this at a flat 50% discount.

ModelStandard PricingBatch PricingSavings
GPT-4o$2.50 / $10.00$1.25 / $5.0050%
GPT-4o-mini$0.15 / $0.60$0.075 / $0.3050%
Claude Sonnet 4$3.00 / $15.00$1.50 / $7.5050%
Claude Haiku 3.5$0.80 / $4.00$0.40 / $2.0050%

Prices approximate. Last updated April 2026.

Ideal Batch API Workloads

Batch processing is ideal for any task where results are not needed immediately:

  • Content generation: Product descriptions, marketing copy, blog drafts at scale
  • Data processing: Categorization, entity extraction, sentiment analysis on large datasets
  • Evaluation: Running test suites against LLMs, A/B testing prompt variations
  • Training data: Generating synthetic training data, creating labeled datasets
  • Bulk analysis: Processing document archives, analyzing customer feedback, summarizing research papers

Implementation Approach

Both OpenAI and Anthropic batch APIs follow a similar pattern:

  1. Create a JSONL file with your requests (one request per line)
  2. Upload the file and create a batch job
  3. Poll for completion (typically 1-24 hours)
  4. Download results as a JSONL file

The key architectural decision is separating batch-eligible work from real-time work in your application. Many teams find that 30-50% of their total LLM usage can be moved to batch processing with some pipeline restructuring.

Combining Batch with Hybrid Routing

For maximum savings, combine batch processing with hybrid routing. Use batch API for all non-time-sensitive workloads (50% savings), then route your remaining real-time traffic through Token Landing's hybrid routing (40-70% additional savings on that portion).

For a team spending $10,000/month on LLM APIs, a typical optimization:

  • Move 40% of traffic to batch: saves $2,000/month
  • Route remaining 60% through hybrid routing: saves $2,400-4,200/month
  • Total savings: $4,400-6,200/month (44-62% reduction)

This is not theoretical — these are the ranges we see in practice with teams using both strategies together.

FAQ

+What is a batch API for LLM?
A batch API lets you submit large sets of requests at once and receive results hours later instead of in real-time. In exchange for slower response times, providers offer significant discounts, typically 50% off standard pricing.
+How much does batch API save?
Both OpenAI and Anthropic offer 50% off all models when using their batch APIs. A request that costs $0.01 in real-time costs $0.005 in batch mode.
+When should I use batch API vs real-time?
Use batch for any workload that does not need immediate responses: data processing, content generation pipelines, evaluation runs, training data preparation, and bulk analysis. Use real-time for user-facing interactions.

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