OpenAI Batch API
OpenAI's asynchronous batch API for processing large volumes of LLM requests at 50% cost reduction with up to 24-hour completion windows.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Batch files stored temporarily by OpenAI — sensitive data in prompts is retained per OpenAI data retention policy.
⚡ Reliability
Best When
Best for high-volume offline processing tasks where 50% cost savings justifies up to 24-hour turnaround.
Avoid When
Avoid when any request needs results in under a minute or when workflow is blocking on LLM output.
Use Cases
- • Classify or label large datasets overnight at half the cost of synchronous API calls
- • Generate embeddings for millions of documents in batch without rate limit pressure
- • Run evals on hundreds of test cases asynchronously as part of CI/CD pipelines
- • Produce structured extractions from large document corpora in cost-efficient batches
- • Annotate training data at scale where immediate response is not required
Not For
- • Real-time agent workflows requiring immediate LLM responses
- • Interactive user-facing applications where latency matters
- • Tasks requiring streaming responses or partial results before full completion
Interface
Authentication
Same API key as standard OpenAI API. Organization and project headers supported.
Pricing
Batch pricing is exactly 50% of synchronous API pricing for all supported models.
Agent Metadata
Known Gotchas
- ⚠ Batch completion can take anywhere from minutes to 24 hours — agents must poll batch status and not block waiting
- ⚠ Input file must be JSONL with one request object per line — each line must include model, messages, and custom_id
- ⚠ Output is also a JSONL file retrieved by file ID — not available via streaming, must download entire file at completion
- ⚠ Failed individual requests appear in the output file with error field, not as batch-level failures — always check each response
- ⚠ Batch enqueue limits are per-model per org — hitting the limit returns 429 but the limit resets as batches complete
Alternatives
Full Evaluation Report
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Scores are editorial opinions as of 2026-03-07.