Anthropic: Claude Sonnet 5.5 (batch)

Anthropic: Claude Sonnet 5.5 (batch) is a text model from Anthropic, served through api14 under the model id anthropic/claude-sonnet-5.5:batch. It belongs to Anthropic's Sonnet class, the tier the provider describes as intended for well scoped everyday work, and it succeeds Claude Sonnet 5 as a direct upgrade. The batch label indicates this is the batch variant of the model rather than the interactive one.

The model accepts text, images and files as input and returns text. Its context window is 1,000,000 tokens, and the maximum output is 128,000 tokens. Anthropic positions Sonnet 5.5 as especially strong at building features, fixing bugs and producing related work, which makes it a reasonable default for coding and general assistant tasks rather than a narrowly specialised model.

Those capabilities point to a fairly wide set of practical uses. Long context plus file and image input suits repository scale code work, such as implementing a feature across several files, tracing and fixing a bug, or reviewing a large diff alongside screenshots and logs. The same combination works for document heavy tasks where a developer needs to reason over long specifications, contracts or reports together with supporting images. Because the model supports tool calling and structured outputs, it also fits agent style workflows that need to call functions and return machine readable results rather than free text.

You call it through api14's OpenAI compatible API using the model id anthropic/claude-sonnet-5.5:batch, so existing OpenAI SDK code works with a change of base URL and model name. 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 with max_tokens, control how much internal reasoning the model does through reasoning and reasoning_effort, request JSON shaped output via response_format or structured_outputs, define callable functions with tools and tool_choice, set stop sequences, and adjust answer length or detail with verbosity. If you want the reasoning trace surfaced in the response, enable include_reasoning.

There are a couple of things to weigh before committing. Output is text only, so if you need generated images or audio this model is not the fit. The batch variant is aimed at workloads that tolerate batch style processing rather than tight interactive loops, so latency sensitive chat or real time agents may be better served by the non batch version of the same model. For very small or simple tasks, a lighter model may be sufficient.