Anthropic: Claude Opus 4.1 (batch)

Claude Opus 4.1 (batch) is a text generation model from Anthropic, offered through api14 under the model id anthropic/claude-opus-4.1:batch. It is an updated version of Anthropic's flagship model, and the provider describes it as offering improved performance in coding, reasoning, and agentic tasks. The batch label indicates this variant is intended for batch style workloads rather than interactive, latency sensitive traffic.

The model accepts image, text, and file inputs, and it produces text output only. It has a context window of 200,000 tokens and can generate up to 32,000 tokens in a single response. Its knowledge cutoff is 2025-01-31, so anything after that date is outside what the model learned during training. Because it handles images and files alongside text, it can work over mixed content such as documents with embedded figures, screenshots, or scanned pages, as long as those inputs are supplied in the request.

Those capabilities point to a fairly specific set of use cases. Coding work is a natural fit, since the model was tuned for it, and the large context window means you can pass whole repositories, long files, or extensive diffs without chunking them first. Reasoning heavy tasks such as multi step analysis, planning, and evaluation also suit it, and the agentic focus makes it a reasonable choice for tool driven workflows where the model decides what to call next. Document understanding over images and files covers tasks like extracting structured data from reports, reviewing contracts, or summarising material that mixes prose and visuals. Batch processing fits offline jobs: large scale classification, bulk summarisation, dataset enrichment, or generating structured records from a queue of inputs where throughput matters more than per request latency.

You call it through api14's OpenAI-compatible API using the model id anthropic/claude-opus-4.1:batch, which means existing OpenAI client libraries and request shapes work with only the model name changed. The supported request parameters are include_reasoning, max_tokens, reasoning, response_format, stop, structured_outputs, temperature, tool_choice, and tools. In practice that covers output length control, sampling behaviour, stop sequences, JSON or schema constrained output through response_format and structured_outputs, and function calling through tools and tool_choice. The reasoning and include_reasoning parameters let you control and surface the model's reasoning behaviour where that is useful.

The main constraint to keep in mind is that this is a batch variant, so it is not the right pick when you need immediate responses in an interactive product. The 32,000 token output ceiling also matters for tasks that need very long single responses, and the January 2025 knowledge cutoff means questions about recent events will need retrieval or context supplied in the prompt. If your workload is latency sensitive, a non batch model would fit better.