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Batch API cuts eligible API costs by 50% while supporting large, latency-tolerant workloads.
Batch queue limits depend on model, usage tier, and total queued input tokens.
Tier 5 GPT-5-family models can reach a 15-billion-token Batch queue.
OpenAI’s Batch API has become an important option for large AI workloads that do not need an instant result. The service lets businesses send a large set of API requests for later execution rather than ask for each result at once. OpenAI sets a 24-hour completion window for Batch jobs, while many jobs can finish sooner. The main financial benefit stands out clearly: Batch API requests cost 50% less than standard synchronous API use.
This price cut can create major savings at scale. A workload that costs $1,000 through the normal API would cost about $500 through Batch, before any differences in model choice or input and output mix. At $10,000 of standard API spend, the nominal Batch cost falls to about $5,000. At $100,000, the same 50% reduction represents about $50,000 in savings.
Batch API does not rely on the same simple request-per-minute view that often guides standard API traffic. OpenAI measures Batch queue limits through the total number of input tokens held in the queue for a specific model. Tokens from pending batches count toward that limit. Once a batch finishes, those tokens leave the active queue.
This detail matters for large AI workloads. A system with a high request-per-minute limit can still face a Batch queue limit if each request carries a large number of input tokens. Large document sets, long prompts and extensive datasets can therefore consume queue capacity much faster than a simple request count suggests.
Current OpenAI model data shows how sharply capacity can rise across usage tiers. GPT-5.5 lists 500 requests per minute, 500,000 tokens per minute and a 1.5 million-token Batch queue at Tier 1. At Tier 2, the limits rise to 5,000 requests per minute, 1 million tokens per minute and a 3 million-token Batch queue.
Tier 3 raises the Batch queue to 100 million tokens, while Tier 4 reaches 200 million. Tier 5 reaches 15 billion tokens, alongside 15,000 requests per minute and 40 million tokens per minute. The move from the 1.5 million-token Tier 1 queue to the 15 billion-token Tier 5 queue represents a 10,000-fold increase.
GPT-5 and GPT-5.4 show the same broad Tier 1 through Tier 5 pattern in their current published limits. These figures make one point clear: there is no single OpenAI Batch limit. Capacity depends on the model and the account’s usage tier.
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Embedding workloads show an even wider capacity range. The current text-embedding-3-small limits list a 3 million-token Batch queue at Tier 1, 20 million at Tier 2, 100 million at Tier 3, 500 million at Tier 4 and 4 billion at Tier 5.
That change takes the available Batch queue from 3 million to 4 billion tokens, or roughly 1,333 times more capacity. Such scale makes Batch well suited to large document sets, search indexes, semantic classification and offline data enrichment.
The 24-hour Batch window creates a simple trade-off. Standard API calls suit tasks that need an immediate result. Batch suits tasks that can wait. This distinction can make a large difference in total API cost.
A company that needs results for a live customer request has little reason to select Batch. A company that needs to classify millions of records overnight has a very different requirement. The lower price and separate queue can make Batch a much better fit for that second case.
Standard API traffic still faces limits such as requests per minute and tokens per minute. A higher budget does not automatically remove those technical limits. Rate limits, spending limits and Batch queue capacity represent different controls.
A large production system therefore needs a clear split between real-time and offline work. Live application requests can use the standard API, while large jobs can move to Batch. This separation can protect interactive traffic from large offline workloads and give each workload a more suitable capacity path.
OpenAI has also widened Batch support during 2026. The API changelog shows Batch support for GPT Image 1.5, ChatGPT Image Latest, GPT Image 1 and GPT Image 1 Mini. OpenAI later added GPT Image 2 with Batch support and the same 50% Batch discount. Batch support has also expanded into video workloads, including Sora API use.
This expansion shows that Batch no longer fits only text classification or embedding tasks. The same offline model now reaches more forms of AI work, including multimodal workloads.
Large Batch systems still need careful job control. OpenAI Community reports from March and May 2026 described cases where some Batch jobs stayed at zero progress or remained in progress for long periods. These reports do not prove a general reliability problem across the service, yet they highlight a real operational concern for large deployments.
A serious Batch setup needs status checks, expiration handling, retry plans and result reconciliation. A large dataset can contain thousands or millions of separate requests, so even a small failure rate can create a meaningful cleanup task.
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The strongest figures tell the story clearly. Batch offers a 50% cost reduction and a 24-hour completion window. Several current GPT-5-family models list a 15 billion-token Batch queue at Tier 5, along with 15,000 requests per minute and 40 million tokens per minute. text-embedding-3-small reaches a 4 billion-token Batch queue at Tier 5.
The central lesson is simple. Large API workloads should not rely only on request counts. Model choice, usage tier, input-token volume, queue capacity and required response time all shape the real throughput available. For workloads that can wait, OpenAI Batch API offers a powerful combination of lower cost and separate capacity. For real-time requests, standard API access remains the more suitable path.
OpenAI Batch API handles large groups of API requests asynchronously within a 24-hour completion window.
Batch API costs 50% less than standard synchronous API use for eligible workloads.
Model selection, usage tier, and the total input tokens in the active Batch queue determine capacity.
Several current GPT-5-family models list a 15-billion-token Batch queue at Tier 5.
Batch API suits large workloads that can wait for results, such as classification, embeddings, evaluations, and large-scale data processing.
