Anthropic vs OpenAI Batch API — which is cheaper for bulk inference?
Teams doing bulk analysis, fine-tuning data prep, log classification, or offline evaluation must choose between Anthropic Message Batches API and OpenAI Batch API. As of 2026-03-16, both APIs are live and both offer a 50% discount on input and output tokens. The decision turns on model family preference, batch size limits, endpoint breadth, ZDR eligibility, and whether prompt-caching can stack on top of the batch discount.
Blockers
- Anthropic Message Batches / batch pricing surfaced via Claude HTTP API.
- OpenAI Batch API pricing and submission via OpenAI HTTP API.
Who this is for
- cost-sensitive
- high-scale
- low-ops
- enterprise
- small-team
- compliance
Candidates
Anthropic Message Batches API
As of 2026-03-16, Anthropic's Message Batches API is live and charges 50% of standard token rates on both input and output for all active Claude models. Verified batch prices include Claude Sonnet 4.6 at $1.50 input and $7.50 output per MTok, Claude Haiku 4.5 at $0.50 input and $2.50 output per MTok, and Claude Opus 4.6 at $2.50 input and $12.50 output per MTok.
When to choose
Best for cost-sensitive + high-scale or low-ops + small-team teams whose workload runs on Claude models, tolerates up to 24-hour turnaround, and benefits from stacking prompt caching on top of the batch discount. The 1-hour prompt cache duration is especially well-suited here because Anthropic's docs explicitly note that batches often exceed the 5-minute cache window. Also favored when batch volume exceeds 50,000 requests per job, since Anthropic's limit of 100,000 requests or 256 MB per batch is double OpenAI's request cap.
Tradeoffs
Anthropic documents that the Batch API discount stacks with prompt caching multipliers, so a batch request with a cache hit costs 0.1x the base input rate at 50% off, meaning a Claude Sonnet 4.6 cache hit in batch mode costs approximately $0.15 per MTok instead of $3.00. The tradeoff is that Anthropic Batch is Messages-only: you cannot batch image generation, video, embeddings, or moderations through this API. The results are scoped to a Workspace and available for 29 days.
Cautions
Anthropic's official docs explicitly state that the Message Batches API is not eligible for Zero Data Retention (ZDR). If your workload has ZDR requirements, batch mode is disqualified regardless of cost savings. Also note that Anthropic's rate limits apply both to batch HTTP requests and to the count of requests within a batch waiting to process, so very large volumes may see batches expire at the 24-hour boundary if demand is high. Batches that expire are not retried automatically.
OpenAI Batch API
As of 2026-03-16, the OpenAI Batch API is live and offers 50% lower costs than standard synchronous APIs on both input and output tokens. Verified supported endpoints include /v1/responses, /v1/chat/completions, /v1/embeddings, /v1/images/generations, /v1/images/edits, /v1/videos, /v1/moderations, and /v1/completions, covering text, image, video, and embedding workloads across models such as gpt-5.4, gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, gpt-4o, and gpt-4o-mini.
When to choose
Best for cost-sensitive + high-scale or low-ops + enterprise teams who are already on the OpenAI stack, need to batch modalities beyond text (images, video, embeddings, moderations), or want a separate rate-limit pool that does not consume synchronous API quota. The 24-hour processing window and up to 50,000 requests per batch at 200 MB max input file size covers most offline pipelines. Also the default choice when the model requirement is a GPT-series model.
Tradeoffs
OpenAI's Batch API has a broader endpoint surface than Anthropic's and is the only batch option for OpenAI's image and video generation models. The rate-limit pool is documented as separate from synchronous APIs with substantially more headroom, which means large batch jobs do not degrade interactive API capacity. The tradeoff is that the 50,000-request cap per batch is half of Anthropic's 100,000-request limit, and each input file can only contain requests for one model, so mixed-model jobs require multiple batch files.
Cautions
Verify model support for your specific model version before building batch pipelines: not every model variant is listed as supported. OpenAI also documents that if a batch expires before completion, unfinished requests are canceled but completed requests within that batch are still billed. For gpt-5.4 and gpt-5.4-pro, OpenAI documents a long-context surcharge above 272K input tokens that applies in batch mode as well as synchronous mode, so large-context batch jobs do not avoid the premium by using Batch.
Hybrid cross-provider batching: Anthropic for text, OpenAI for multimodal
Route text-only bulk jobs to whichever provider matches your model preference at 50% off, and reserve OpenAI Batch for multimodal or embedding workloads that Anthropic Batch does not support. As of 2026-03-16, both APIs offer equal discount depth, so provider choice for text is driven by model quality, context window, and operational constraints rather than discount rate.
When to choose
Best for enterprise + high-scale or microservices + cost-sensitive teams running diverse offline pipelines where some jobs need Claude's reasoning or long context (up to 1M tokens in batch mode on Claude Sonnet 4.6 and Opus 4.6) while other jobs need GPT-series image generation, video synthesis, or embeddings. Use Anthropic Batch when batch size exceeds 50,000 requests or when stacking prompt caching is material; use OpenAI Batch for every other modality.
Tradeoffs
This approach maximizes coverage and lets you pick the best-fit model per task type without paying a latency premium. The tradeoff is dual-provider operational overhead: two sets of API keys, two billing accounts, two polling implementations, and two result-storage pipelines. You must also track ZDR ineligibility on the Anthropic side and model-file-per-batch restrictions on the OpenAI side.
Cautions
Do not assume pricing parity will persist indefinitely. Both providers price batch at 50% off standard as of 2026-03-16, but standard token prices differ per model and change independently. Budget models by anchoring to actual per-model batch prices on the verified pricing pages rather than assuming a uniform effective rate. Also, Anthropic Batch is not ZDR-eligible; if any portion of your routing falls under compliance requirements, do not send those requests to Anthropic Batch even in a hybrid setup.
Try with your AI agent
$ npm install -g pocketlantern $ pocketlantern init # Restart Claude Code, Cursor, or your MCP client, then ask: # "Anthropic vs OpenAI Batch API — which is cheaper for bulk inference?"