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.
Transparent by design — computed in your browser from a published formula and sourced rates, not a black box. Data verified May 25, 2026. Sources: Anthropic pricing ↗ · OpenAI pricing ↗ · Google AI / Gemini pricing ↗ Full methodology →
- Inputs
- Form inputs / CSV
- Runtime
- Instant
- Privacy
- Client-side · no upload
- API key
- Not required
- Methodology
- Open →
1 · Configure the workload
Savings unavailable
0%
$0.00000/month saved · Real-time · 50% off · SLA 24h
Real-time: $33.75/day · Batch: $16.88/day · Effective: $33.75/day
Mode suggestion
Real-time required — deadline 12h < 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 4.5 | $11.25 | $5.63 | 24h | no |
| openai | GPT-5.4 mini | $9.00 | $4.50 | 24h | no |
| Gemini 2.5 Flash-Lite | $1.05 | $0.525 | 24h | no |
"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
Pricing and batch SLAs verified 2026-04-23 against vendor docs. See methodology for sources and when batch is not a valid choice.
How to use
Step-by-step
- 1
Enter your call volume per period, prompt length, output length, and model.
- 2
Set your latency tolerance: 0 means realtime only; 24h means batch is acceptable.
- 3
Read the cost comparison: realtime price vs. batch price (50% discount on supported APIs).
- 4
Read the rate-limit comparison. Some workflows that exceed realtime rate limits work fine in batch.
- 5
Use the breakeven view: at what volume does batch's setup overhead pay for itself? Typically ~50 calls/job for most workloads.
For agents
Use in an agent
Same math, same result shape as the UI above — as a static ES module. No HTTP request, no auth, no rate limit.
import { compute } from "https://aifinhub.io/engines/batch-vs-realtime-cost-calculator.js";Contract: /contracts/batch-vs-realtime-cost-calculator.json Full agent guide →
Glossary references
Terms used by this tool
Questions people ask next
FAQ
What's batch vs. realtime?
Realtime API calls execute as you make them, with full pricing. Batch APIs (Anthropic, OpenAI both offer them) accept a job of many prompts, run them within 24 hours, and discount by 50%. The calculator shows when batching is worth the latency tradeoff.
When is batching worth it?
When you can tolerate up to 24 hours of latency. The methodology page documents three patterns: nightly research runs (100% batch-compatible), daily morning ingestion (batch-compatible if you start the job evening before), and sub-hour analytics (realtime only). Batching saves 50% on input + output tokens.
Are there volume limits?
Yes — current limits are documented on the methodology page. Anthropic batch caps at 100,000 requests per job; OpenAI at 50,000. Both have rolling concurrency limits. Higher-volume workflows split into multiple batches. The calculator reports whether your volume fits a single batch.
Does batching change quality?
No — same models, same parameters, same outputs. The only differences are latency and price. The methodology page confirms this with a benchmark: identical prompts run through batch and realtime produce indistinguishable outputs at temperature=0.
What about rate limits?
Realtime has tight per-minute rate limits that constrain throughput; batch has soft daily caps. For a workflow that would hit rate limits in realtime, batching is often the only path even if you'd prefer the latency. The calculator surfaces this constraint.
Related deep dive
All articles →Read further
Long-form context behind the tool output.
- Methodology · Opinion·11 min
Prompt Caching Economics for Finance
How Anthropic, OpenAI, and Gemini prompt caching works on finance workloads — 5-minute TTL, hit-rate patterns, and 50-90% input savings at the right design.
Read - Methodology · Opinion·11 min
Batch API Economics for Finance Loops
When Anthropic Message Batches or OpenAI Batch cut cost by half on finance workloads — and the soft-deadline rule for when batch is not a valid choice.
Read - Tutorial · Runnable·11 min
Inference Cost Attribution per Idea and Trade
Append-only cost-event schema plus two canonical SQL queries — cost per idea, cost per validated trade — with cache-write amortization built in.
Read
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