# Comparing Batch and Real-Time LLM Inference Economics > Source: > Published: 2026-11-04 12:11:17+00:00 # Comparing Batch and Real-Time LLM Inference Economics Batch inference costs 50% less than real-time inference at Anthropic, OpenAI and Google, in exchange for results that can take up to 24 hours. For a workload of one million document classifications a month on a mid-tier model, that is roughly $7,000 a month in real time versus $3,500 in batch, about $42,000 a year saved (illustrative calculation below). The price difference reflects one tradeoff: the provider decides when your request runs. All three major API providers price batch at half of standard, so the decision is about workload fit, not vendor choice. Anthropic: Message Batches cost 50% of standard prices on input and output tokens. Prompt caching discounts stack with batch pricing, though cache hits in batch are best-effort, typically 30–98% depending on traffic (Anthropic batch docs). Example batch prices: Claude Sonnet 5.5 at $1 input and $5 output per million tokens; Claude Opus 5.5 at $2 and $10. Standard prices are double. OpenAI: launched its Batch API in April 2024 at a 50% discount with a completion window of up to 24 hours (Simon Willison). Flex processing gives Batch API rates on synchronous calls, and prompt caching can add further discounts (OpenAI docs). When many requests share a long prefix, such as a system prompt, policy document or product catalog, the provider can reuse it. Reading cached tokens costs a small fraction of normal input price: 10% of base input on standard Anthropic models, with lower multipliers reported for some newer models, and a premium of 1.25 to 2 times base input to write the cache. This model uses the conservative 10% read price. Verify current multipliers before budgeting. Cost model: three workloads (illustrative) \text{Monthly cost} = \frac{\text{requests} \times \text{input tokens}}{10^6} \times p_{in} + \frac{\text{requests} \times \text{output tokens}}{10^6} \times p_{out} 1 million documents a month, 2,000 input tokens and 300 output tokens each. Nobody waits on individual results. 500,000 conversations a month, 4,000 input tokens each (of which 3,000 are a shared system prompt and knowledge snippet), and 400 output tokens. Customers wait live, so batch is not an option. 200,000 requests a month, run overnight, 10,000 input tokens each (8,000 shared context) and 1,000 output tokens. Results are needed by morning. Notice what dominates once input is discounted: output tokens. In Scenario 3, output is 44% of the batch-plus-cache bill. Shorter, structured outputs are often the next lever. If you run an open-weight model on rented GPUs, you pay for GPU hours whether or not requests arrive. Batch workloads matter here for a different reason: they keep GPUs busy. Ask one question of every AI call: is a person or a live process waiting for this answer right now? Classifying, tagging or extracting fields from documents, tickets and records Chat assistants and customer support Work that can wait minutes but not hours, such as a background summary a user checks later, fits flex tiers. They give batch-level prices on ordinary synchronous calls, but requests can be refused or delayed, so your code needs a fallback to standard. Most production systems end up with three lanes. Interactive traffic runs real-time with prompt caching. Deferrable traffic goes into a queue that submits batches on a schedule. Anything urgent that a flex call cannot serve falls back to standard. The engineering is modest, and the savings apply to every token in the deferrable lane. The batch discount is only real if the pipeline around it is reliable; budget engineering time for these pieces. Disclosure: the author works with CodingCops. This section uses only the company's published descriptions and includes no CodingCops pricing, client results or savings claims. Anthropic, OpenAI and Google all price batch at 50% of standard rates, as of November 2026. Up to 24 hours at all three providers. Anthropic says most batches finish within an hour; requests not done within 24 hours expire. At Anthropic, yes: the discounts stack, though cache hits in batch are best-effort. Check each provider's current docs. A synchronous tier priced like batch. Calls are slower and may be refused when capacity is short, so you need a fallback. OpenAI and Google both offer one. Only at sustained high volume with high GPU utilization, and after counting the engineers who run it. Idle GPUs make self-hosted tokens expensive quickly. It depends entirely on the product. Audit your own traffic: anything nobody waits on is a candidate. ## Key Takeaways - •The price difference reflects one tradeoff: the provider decides when your request runs. - •This story was reported by **Dev.to** , covering developments in the**dev** space. - •AI advancements continue to reshape industries — read the full article on Dev.to for complete coverage. 📖 Continue reading the full article: [Read Full Article on Dev.to →](https://dev.to/ericthompson21/comparing-batch-and-real-time-llm-inference-economics-3fmi)