Anthropic: Claude Opus 4.1

Claude Opus 4.1 is a text model from Anthropic, listed on api14 as anthropic/claude-opus-4.1. It is an updated version of Anthropic's flagship model, released by the provider on 5 August 2025, and it sits in the text category. Anthropic describes it as offering improved performance in coding, reasoning, and agentic tasks compared with the previous Opus release, and it reports 74.5% on SWE-bench Verified. Its knowledge cutoff is 31 January 2025.

The model accepts text, images, and files as input, and it returns text only. It has a context window of 200,000 tokens and can produce up to 32,000 tokens in a single response. Because it takes images and files alongside text, you can send screenshots, diagrams, or documents in the same request as your instructions, which suits tasks where the model needs to read something before answering. Reasoning is exposed as a controllable behaviour rather than a fixed one: the supported parameters include reasoning and include_reasoning, so you can decide whether reasoning is used and whether it is surfaced in the response.

Those capabilities point to a fairly specific set of uses. The coding and agentic focus makes it a reasonable fit for repository-level work such as reading files, proposing patches, and driving multi-step tool calls, especially with the tools and tool_choice parameters available. The 200,000 token context helps when a task depends on a large amount of source material, for example a long specification, a set of related files, or a lengthy transcript. Image input extends this to tasks where the model must interpret a chart, a UI screenshot, or a scanned page before producing text. The 32,000 token output ceiling supports long generated artefacts such as full files, detailed reports, or extended explanations.

You call it through api14's OpenAI-compatible API using the model id anthropic/claude-opus-4.1, so existing OpenAI-style client code generally works with a change of model id and base URL. The request parameters it supports are include_reasoning, max_tokens, reasoning, stop, temperature, tool_choice, tools, top_k, and top_p. In practice, max_tokens caps the response length, temperature, top_p, and top_k shape sampling, stop sets stopping sequences, and tools with tool_choice enables function calling. Use reasoning and include_reasoning when you want the model to think through a problem and, optionally, return that reasoning.

There are limits worth noting before you commit. Output is text only, so if you need generated images, audio, or other media, this model is not the right choice. The knowledge cutoff of 31 January 2025 means anything after that date has to come from your prompt, your files, or a tool call rather than from the model's own knowledge. If your workload is simple and does not need the coding, reasoning, or agentic strengths described here, a smaller text model may be a better fit.