Anthropic: Claude Opus 5 (batch)

Claude Opus 5 (batch) is a text model from Anthropic, served through api14 under the model id anthropic/claude-opus-5:batch. Anthropic describes Claude Opus 5 as its flagship model, built for demanding reasoning, coding, and long-horizon agentic work, and the batch variant is the version you call through this endpoint. The provider release date is 2026-07-24, so it is a recent addition to the Claude line rather than a long-established one.

The model accepts text, images, and files as input, and returns text only. Its context window is 1,000,000 tokens, and the maximum output is 128,000 tokens. That combination of a very large context and a large output budget means you can feed in long documents, whole repositories of source files, or extended conversation histories in a single request and still leave room for a substantial generated response. Because images and files are accepted alongside text, visual analysis and document review fit naturally into the same request shape as ordinary prompts.

The capabilities Anthropic highlights point to a few practical workloads. End-to-end software tasks, such as implementing a feature across several files or carrying a change through from plan to patch, are a direct fit. Code review and bug finding suit the large context window, since you can supply the surrounding code rather than a fragment. Visual analysis lets you send screenshots, diagrams, or scanned pages together with a question about them. Long-horizon agentic work, where the model takes many steps while holding earlier results in context, is the use case the model was positioned around, and it is also where the reasoning and tool parameters below matter most.

To call it, send a request to api14's OpenAI-compatible chat completions endpoint with the model set to anthropic/claude-opus-5:batch. The supported request parameters are include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, stop, structured_outputs, tool_choice, tools, and verbosity. In practice, tools and tool_choice drive function calling, structured_outputs and response_format shape the reply into a schema you can parse, max_tokens and stop bound the generation, and reasoning, reasoning_effort, include_reasoning, and verbosity control how much internal reasoning is requested and how much of it is surfaced back to you.

Nothing in the model's specification rules it out for general text work, but the parameter set and the described strengths suggest where it earns its place. If you need an image or audio output, this model returns text only, so you would look elsewhere. If your task is short and does not need tool use, structured output, or extended reasoning, a lighter model would likely serve you just as well.