passdrill
AI & LLM Engineering · Building with LLM APIs · Card 012/012 hard

A team needs to run sentiment classification over 40,000 archived support tickets and is not latency-sensitive, but wants to minimize per-request cost. According to OpenAI's Batch API documentation, what tradeoff does using the Batch API (instead of the standard synchronous chat completions endpoint) involve?

  1. The Batch API charges the same per-token price as the synchronous API but guarantees results within 60 seconds regardless of batch size
  2. The Batch API is free of charge for any volume of requests, but results are only available after a mandatory 7-day waiting period
  3. The Batch API only accepts a single request per batch, so the team would still need to submit 40,000 separate batch jobs to process all the tickets
  4. The Batch API offers roughly a 50% cost discount compared to the synchronous API, but processes the submitted batch of requests asynchronously with results typically available within 24 hours rather than immediately, and draws from a separate rate-limit pool
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