Anthropic: Claude Opus 5.5 (batch)

Claude Opus 5.5 (batch) is a text model from Anthropic, offered through api14 under the model id anthropic/claude-opus-5.5:batch. It is the batch variant of Anthropic's flagship Claude Opus 5.5, which the provider describes as succeeding Claude Opus 5 and as being built for demanding reasoning, coding, and long-horizon agentic work. The provider release date is 2026-09-22.

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 is 128,000 tokens. That combination of a very large context and a large output budget means you can supply long documents, large codebases, or extended conversation histories in a single request and still receive a substantial response. Because images and files are accepted alongside text, you are not limited to plain string prompts.

The capabilities point to a few practical uses. Multi-step changes across large codebases are a stated strength, so refactoring, migration work, and cross-file edits are reasonable fits. Long-horizon agentic work suits tool-driven pipelines where the model plans, calls tools, and continues over many turns. Long-context reasoning over large inputs fits document analysis, review of lengthy specifications, and question answering over material that would not fit in a smaller window. Image and file input extend this to tasks where the source material includes screenshots, diagrams, or attached documents.

You call it through api14's OpenAI-compatible API using the model id anthropic/claude-opus-5.5: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 that means you can cap output length with max_tokens, control reasoning behaviour through reasoning and reasoning_effort (and surface it with include_reasoning), constrain output with response_format and structured_outputs, define and select tools with tools and tool_choice, set stop sequences, and adjust verbosity. If you already use an OpenAI-compatible client, the main change is the model id and the parameters you choose to send.

One thing to keep in mind is that this is the batch variant, so it is aimed at workloads that tolerate batch processing rather than interactive, low-latency exchanges. If your application needs immediate turn-by-turn responses, a non-batch model is likely a better fit. The model is also text-only on the output side, so tasks that require generated images or audio fall outside its scope.