Anthropic: Claude Opus 4.6 (batch)

Claude Opus 4.6 (batch) is a text generation model from Anthropic, served through api14 under the model id anthropic/claude-opus-4.6:batch. Anthropic positions Opus 4.6 as its strongest model for coding and long-running professional tasks, built for agents that operate across entire workflows rather than single prompts. The batch variant is the same model offered through a batch processing mode, which suits workloads where you can submit work and collect results rather than needing an immediate response on every call.

The model accepts text, images and files as input, and it produces text as output. Its context window is 1,000,000 tokens, and the maximum output length is 128,000 tokens. That combination of a very large input window and a large output ceiling means you can hand it long documents, large codebases or multi-part file sets in a single request and still get back a substantial, complete response rather than a truncated one. Because it takes images and files alongside text, it can work over mixed material such as screenshots, diagrams, PDFs and source files within the same conversation.

Those capabilities point to a fairly specific set of use cases. Coding work is the clearest fit: repository-scale review, refactoring plans, test generation and debugging across many files, since the context window can hold a large amount of source at once. Long-running professional tasks follow from the same strengths, for example document analysis over lengthy contracts or reports, extraction and summarisation across file collections, and agent-style pipelines where the model plans, calls tools and iterates over several steps. The batch mode is a natural match for offline jobs such as bulk classification, dataset annotation, report generation and large-scale code or document processing, where throughput matters more than per-request latency.

Calling it through api14 works like any other OpenAI-compatible endpoint: send a request to the chat completions route with the model field set to anthropic/claude-opus-4.6:batch. The supported request parameters are include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, stop, structured_outputs, temperature, tool_choice, tools, top_p and verbosity. In practice that means you can control sampling with temperature and top_p, cap output with max_tokens, constrain output with response_format or structured_outputs, stop generation on given sequences with stop, and build tool-using agents with tools and tool_choice. Reasoning behaviour is tunable through reasoning, reasoning_effort, include_reasoning and verbosity.

The main thing to weigh is the batch mode itself. It is designed for submitted workloads rather than interactive, low-latency chat, so if you need immediate responses for a user-facing assistant, a non-batch deployment of the same model is the better fit. Likewise, if your task is short and simple, a smaller model will usually be sufficient and faster to work with. Choose this model when the job is genuinely large: long context, heavy coding or document work, multi-step agents, or bulk offline processing where the 1,000,000 token window and 128,000 token output limit are actually useful.