Batch-Api

Claude’s Extended Output beta raises the max_tokens ceiling from 128K to 300,000 tokens — but only for requests sent through the Message Batches API. If you’re generating full codebases, book-length documentation, or exhaustive structured extractions in a single turn, this guide covers everything you need to get it working. What Is Extended Output and How Does It Work? Extended Output is a Claude API beta feature, activated via the anthropic-beta: output-300k-2026-03-24 header, that increases the maximum max_tokens limit per request from 128,000 to 300,000 tokens. As of June 2026, it is only available on the Message Batches API — the synchronous Messages API remains capped at 64K–128K depending on the model. The models that support extended output are Claude Opus 4.8, Opus 4.7, Opus 4.6, and Sonnet 4.6, all of which carry 1M-token context windows. Claude Fable 5 and Mythos 5 are explicitly excluded and remain at 128K output. A single 300K-token generation can take over an hour to complete, which is why the asynchronous batch architecture is a prerequisite. This is not a setting you flip on a chat endpoint — it’s a deliberate architectural tradeoff: accept latency, gain volume. The practical upside is book-length code scaffolds, full API documentation sets, and exhaustive data extraction jobs that previously required chaining multiple requests with fragile state management between them. ...

Gemini Flash-Lite Batch API cuts your LLM costs in half by processing requests asynchronously — submit a JSONL file, get results back within 24 hours, and pay $0.125/1M input tokens instead of $0.25. For teams running thousands of daily classification, translation, or summarization jobs, this single change can reduce monthly AI spend from hundreds of dollars to tens. What Is the Gemini Batch API and Why Does It Matter The Gemini Batch API is Google’s asynchronous processing mode that applies a 50% discount on all paid Gemini models for non-real-time workloads. Instead of sending individual HTTP requests and waiting for each response, you package hundreds or thousands of requests into a JSONL file, submit it as a batch job, and retrieve results once the job completes — typically well under 24 hours. Launched alongside the Gemini 3 family in early 2026, the Batch API targets the large class of AI tasks where latency is irrelevant: overnight content moderation queues, bulk data extraction pipelines, weekly report generation, and offline document analysis. The mechanism is simple: Google processes your batch during off-peak capacity windows, passes the savings directly to you, and guarantees completion within one day. For startups and enterprises alike, this transforms formerly expensive batch pipelines into genuinely affordable infrastructure. At $0.125/1M input tokens with Flash-Lite, you can process an entire Wikipedia-scale corpus for under $10 — a threshold that makes previously cost-prohibitive use cases like fine-tuning dataset generation or full-catalog product description rewrites financially viable. ...

GPT-5.5 Batch API and Flex mode both offer 50% off standard pricing — $2.50 per 1M input tokens and $15 per 1M output tokens versus the standard $5/$30 — giving high-volume AI coding teams a direct path to halving their monthly API spend without changing models or degrading output quality. What Is GPT-5.5 Batch API and Flex Mode? GPT-5.5 Batch API and Flex mode are two distinct pricing and execution tiers from OpenAI that both deliver 50% cost savings compared to standard API rates, but differ significantly in how and when results are returned. The Batch API is a fire-and-forget system: you submit up to 50,000 requests in a single JSONL file (up to 200MB), and OpenAI guarantees results within 24 hours. Flex mode, currently in beta as of April 2026, is interactive — requests are processed in real time but with variable latency ranging from a few seconds to several minutes, depending on platform load. GPT-5.5 launched on April 23, 2026, at standard pricing of $5 per 1M input tokens and $30 per 1M output tokens. Both Batch and Flex bring that cost down to $2.50/$15 — the same price as GPT-5.4 standard, but with GPT-5.5’s higher capability, including an 82.7% score on Terminal-Bench 2.0 and 58.6% on SWE-Bench Pro. For engineering teams running nightly code reviews, eval pipelines, or test generation jobs, the practical implication is straightforward: you get a better model at the same cost you were already paying. ...