Anthropic: Claude Sonnet 4.5 (batch)

Anthropic: Claude Sonnet 4.5 (batch) is a text generation model from Anthropic, offered through api14 under the model id anthropic/claude-sonnet-4.5:batch. It belongs to the Sonnet line, which Anthropic positions as its most advanced Sonnet model to date, optimized for real-world agents and coding workflows. The batch variant is the same model served through a batch workflow rather than an interactive one, which makes it a fit for work that does not need an immediate reply.

The model accepts text, images and files as input and returns text. Its context window is 1,000,000 tokens, and it can produce up to 64,000 output tokens per request. 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, you can send screenshots, documents and mixed content in the same conversation rather than converting everything to plain text first.

Those capabilities point to a few practical patterns. Coding workflows are a natural fit: the model was tuned for them, and the large context window lets you include whole repositories, long diffs or extensive logs in a single request. Agent-style tasks also make sense, since the model supports tool calling and structured outputs, which are the building blocks for loops that read data, call functions and act on results. Document and image understanding is another use case: you can pass PDFs, screenshots or scanned pages and ask for extraction, summarization or classification. The batch mode suits jobs that run over many items, such as bulk summarization, dataset labeling or overnight code review, where throughput matters more than latency.

To call it, send a request to api14's OpenAI-compatible chat completions endpoint with the model id anthropic/claude-sonnet-4.5:batch. Because the API is OpenAI-compatible, existing OpenAI SDKs and clients work with only the base URL and model id changed. The supported request parameters are include_reasoning, max_tokens, reasoning, response_format, stop, structured_outputs, temperature, tool_choice, tools, top_k and top_p. In practice that means you can control sampling with temperature, top_p and top_k, cap output with max_tokens, constrain replies with response_format or structured_outputs, define functions with tools and tool_choice, and manage reasoning behavior with reasoning and include_reasoning.

Two things are worth checking before you commit. The batch variant is not the right choice when you need a response within a normal interactive request, since batch processing is designed for deferred work; for chat or live agent steps, use the standard non-batch model instead. Also, the knowledge cutoff of 2025-01-31 means the model has no built-in awareness of events after that date, so anything recent has to come from the prompt, retrieved documents or tool results. If your task is purely text and small in scope, a lighter model may be enough, but for long-context coding, document-heavy input or tool-driven agents, this model covers those needs directly.