Batch vs Realtime Cost
Estimate how much you save by sending batchable jobs to discounted batch APIs instead of realtime list prices. Pair with LLM Cost for prompt sizing and Cache Savings for the interactive path.
- 1Fill inputs
- 2Run
- 3Copy / export
Inputs
Required fields on the left
0.5 ≈ OpenAI Batch · 0.5 Anthropic Message Batches
Free local math from list prices. Pair with LLM Cost and Cache Savings.
Results
Appears after you run
Price the overnight queue
Enter tokens, batch discount, and volume to see monthly $ saved vs realtime list price.
From inputs to a decision
Estimate how much you save by sending batchable jobs to discounted batch APIs instead of realtime list prices. Pair with LLM Cost for prompt sizing and Cache Savings for the interactive path.
- 01
Enter input and output tokens per request.
- 02
Set the batch price multiplier (0.5 ≈ half of list).
- 03
Add batchable RPS and models to compare.
- 04
Export the monthly realtime vs batch table.
Tips for better results
- Keep chat and user-facing latency on realtime; batch overnight evals, embeddings backfills, and report generation.
- OpenAI Batch and Anthropic Message Batches are commonly ~50% of list — confirm your provider’s multiplier.
- Use the same prompt pack and RPS when comparing models.
Frequently asked questions
- Does batch always cost half?
- No. The multiplier is an input. Start at 0.5 for OpenAI-style Batch, then adjust if your contract differs.
- Can I combine batch with prompt caching?
- Usually not on the same request. Use Cache Savings for realtime sticky prefixes and this tool for deferred volume.
Related tools
Keep measuring in the same cluster — or jump to the next decision.
- LLM Cost CalculatorSide-by-side monthly token cost across DeepSeek, OpenAI, Anthropic, Gemini, and vLLM.Open →
- Prompt Cache SavingsProject monthly $ saved when a reusable prompt prefix hits the provider cache.Open →
- RAG Cost EstimatorProject monthly embedding + retrieval + generation spend for a RAG stack.Open →
- Prompt Token DiffCompare two prompts on tokens and monthly $ across models.Open →