Anthropic: Claude Sonnet Latest

Claude Sonnet Latest is a text model from Anthropic, exposed on api14 under the model id ~anthropic/claude-sonnet-latest. As the name and provider description indicate, this entry always redirects to the latest model in the Claude Sonnet family, so a request made today and a request made after a future Sonnet release will resolve to different underlying versions. That behaviour is useful if you want to track Anthropic's newest Sonnet work without editing your integration each time, but it also means the exact model behind the id can change without notice.

The model accepts text, images and files as input and returns text only. It has a context window of 1,000,000 tokens and can produce up to 128,000 output tokens in a single response. The large context is the headline capability here: you can supply very long documents, whole codebases or extended conversation histories in one request rather than chunking them. Image and file inputs make it suitable for tasks where the source material is not plain text, such as reading scanned pages or screenshots alongside written instructions.

Those capabilities point to a fairly clear set of use cases. Long document analysis, contract and report review, and question answering over large knowledge bases all benefit from the million token window. Because the model accepts images and files, it also fits workflows that mix formats, for example extracting structured data from a PDF plus a diagram, or summarising a support thread that includes screenshots. The 128,000 token output ceiling supports long form generation, such as drafting lengthy technical documentation or producing extended code in one pass, rather than stitching together many short completions.

You call it through api14's OpenAI-compatible API using the model id ~anthropic/claude-sonnet-latest, so existing OpenAI SDK clients work with a changed base URL and model string. The supported request parameters are include_reasoning, max_completion_tokens, max_tokens, reasoning, reasoning_effort, response_format, stop, structured_outputs, tool_choice, tools and verbosity. In practice that means you can cap output length, control how much reasoning effort the model applies, request structured or JSON-shaped responses, define tools and force or guide tool selection, and tune verbosity. Tool calling and structured outputs are the two that matter most for agent and pipeline work, since they let you get machine-readable results instead of parsing free text.

One thing to keep in mind is the redirect behaviour. If your application depends on a fixed model version for reproducibility, evaluation or compliance, this id is a poor fit because the underlying model can change; a pinned Sonnet version would be the safer choice. The same applies if you need output modalities other than text, since this model returns text only. For straightforward text tasks within a smaller context, a lighter model may also be sufficient, though the facts here do not specify which alternatives exist.