Calculator
Batch vs Real-Time Cost Calculator
Batch vs real-time cost calculator for LLM finance workloads: set jobs, tokens, deadline — see daily and monthly batch API savings vs direct.
Rates from aidevhub.io model catalog (each price verified on the vendor page), Anthropic pricing, OpenAI pricing and Google AI / Gemini pricing, checked on October 9, 2026.
Runs in your browser. Nothing you enter is uploaded, and no account or API key is needed.
1. Configure the workload
LLM requests per day.
Batch needs at least the vendor's 24 h window.
Saved per month with batch
$338
50% off: $675 real-time vs $338 batch per month (30 days).
Per day: real-time $22.50, batch $11.25, you pay $11.25. Batch-eligible — deadline 24h >= batch SLA 24h.
2. Per-provider comparison (cheapest model per provider at your workload)
| Provider | Model | Real-time / day | Batch / day | SLA | Deadline OK? |
|---|---|---|---|---|---|
| anthropic | Claude Haiku 5.5 | $1.13 | $0.563 | 24h | yes |
| openai | GPT-6 Luna | $1.13 | $0.563 | 24h | yes |
| Gemini 3.5 Flash-Lite | $4.13 | $2.06 | 24h | yes |
"Deadline OK?" flips to no when your deadline is shorter than the vendor's batch SLA — in that case batch is forced off the table and you pay real-time prices.
How the math works
cost_per_job_realtime = input × price_in + output × price_out cost_per_job_batch = cost_per_job_realtime × (1 - batch_discount) cost_per_day = cost_per_job × jobs_per_day use_batch = supports_batch AND deadline_hours >= batch_sla_hours savings_per_day = use_batch ? realtime - batch : 0
List prices and batch terms verified 2026-10-03 against each vendor's pricing and batch documentation. The SLA is the vendor's maximum completion window; many batches finish sooner, but only the maximum is guaranteed.
How to use it
- Pick the model, then enter jobs per day and the input and output tokens per job.
- Set the deadline: how many hours your results can wait. Batch is only used when the deadline is at least the vendor's batch window (24 hours for every model in the table).
- Read the monthly saving, plus real-time versus batch cost per day and per month. Batch is billed at 50% of list price for input and output.
- If the saving shows $0, the line under it says what a 24-hour deadline would save, so you can decide whether the work can move overnight.
- Use the per-provider table to compare the cheapest Anthropic, OpenAI and Google model for the same workload, in both modes.
The method, step by step
Decide between batch and real-time LLM calls by the deadline of each workload: route work that can tolerate a delay of hours, like overnight research or bulk extraction, to a batch API for a substantial discount, and keep work that a user or a trade is waiting on in real time. The decision is per workload, not global, and the savings only apply where latency genuinely does not matter.
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Sort workloads by their true deadline
List every LLM workload and tag each with when its result is genuinely needed, not when it would be nice to have. Overnight research, end-of-day summaries, and bulk filing extraction usually have deadlines measured in hours. A live signal, a user query, or an intraday alert has a deadline measured in seconds. This sort is the whole decision: deadline determines track, and most pipelines have more deferrable work than teams assume.
Be honest about the deadline. Teams default everything to real-time out of habit, leaving the batch discount on the table for work that could easily wait.
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Route deferrable work to batch
Send the latency-tolerant workloads to a batch API. The batch track typically delivers results within a window of hours at a meaningful discount versus real-time pricing. For high-volume deferrable work like processing a universe of filings or summarizing every earnings call in a sector, the batch discount applied across the whole volume is a substantial recurring saving with no change in output quality, since the same model produces the same result.
Batch shines on high-volume deferrable jobs. The bigger the workload and the more relaxed the deadline, the more the discount is worth.
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Keep waiting-on work real-time
Anything a person or a trade is actively waiting on stays on the real-time track regardless of cost, because missing the window is worse than paying full price. A live trading signal that arrives hours late is useless, and a user staring at a spinner is a bad experience. Do not try to squeeze these into batch to save money; the value of timeliness exceeds the discount. The real-time path is for work where latency is part of the value.
Never batch a workload on the critical path of a live decision. The discount is irrelevant if the answer arrives after the moment it was needed has passed.
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Estimate the savings before committing
Quantify the split: for each deferrable workload, compute the real-time cost and the batch cost side by side, scaled by volume, to see the actual saving. This tells you which workloads are worth the operational effort of a batch pipeline and which are too small to bother. The estimate also surfaces batch-eligibility constraints, like maximum job size or turnaround windows, that might exclude a workload you assumed qualified.
Some deferrable workloads are too small for batch to be worth the operational overhead. Estimate the saving first, then move only the workloads where it clearly pays.
Sources: Message Batches API (Anthropic) ; Prompt Caching with Claude (Anthropic (2024))
Mistakes that undo the result
Defaulting all work to real-time
Most pipelines have more deferrable work than teams realize. Running overnight and bulk jobs at real-time prices out of habit forfeits a large discount for no benefit, since those results are not needed immediately.
Batching work on the critical path
A result that arrives hours late is worthless for a live signal or a waiting user. The batch discount cannot compensate for missing the window the work was needed in, so latency-critical work must stay real-time.
Ignoring batch-eligibility constraints
Batch APIs have limits on job size and turnaround. Assuming a workload qualifies without checking can lead to a job that does not fit the batch window or exceeds size limits, breaking the plan late.
Questions people ask
What's batch vs. realtime?
Real-time API calls return as you make them, at list price. Batch endpoints (Anthropic Message Batches, the OpenAI Batch API, Gemini batch mode) accept many requests at once, complete them within a window of up to 24 hours, and bill input and output at 50% of list price. The calculator prices your workload both ways and applies batch only when your deadline allows it.
When is batching worth it?
Whenever results can wait for the batch window. Overnight research runs, end-of-day filing digests and weekly backfills fit; anything a trader or agent waits on in the moment does not. The calculator treats batch as usable only when your deadline is at least the vendor's 24-hour window, because that is the only completion time the vendors commit to, even though many batches finish sooner.
Are there volume limits?
Yes, per batch: Anthropic documents a cap of 100,000 requests (or 256 MB) per Message Batch, and OpenAI 50,000 requests per batch file. Larger workloads are split across several batches. The calculator does not check these caps, so split your daily job count yourself if it exceeds them; the per-job price is the same either way.
Does batching change quality?
It should not: a batch request runs the same model with the same parameters as a real-time call, and only the scheduling and the price differ. The tool does not measure output quality, so if your workflow is sensitive to it, compare a sample of batch and real-time outputs on your own prompts.
What about rate limits?
Real-time endpoints enforce per-minute request and token limits, while batch work is queued and processed against separate batch limits, so a backfill that would trip real-time limits can often run as a batch. The calculator models price and deadline only; it does not check your account's rate limits.
The same numbers as a table
- Reference Batch vs realtime savings reference grid
Computed by this tool's engine across common inputs, to cite or download as CSV.
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Use it from code
The same calculation as a JavaScript module you can import. It runs where you import it, with no request, key or rate limit.
import { compute } from "https://aifinhub.io/engines/batch-vs-realtime-cost-calculator.js";