Anthropic: Claude Opus 4.5

Claude Opus 4.5 is a text model from Anthropic, listed on api14 as anthropic/claude-opus-4.5. Anthropic positions it as its frontier reasoning model, optimized for complex software engineering, agentic workflows, and long-horizon computer use. It was released by the provider on 2025-11-24. If you are choosing a model for demanding reasoning work rather than simple text generation, this is the tier Anthropic builds for that purpose.

The model accepts text, image, and file inputs, and it returns text. Its context window is 200,000 tokens, and the maximum output is 64,000 tokens. That combination matters in practice: a large context lets you supply long source files, documentation, or multi-step conversation history in a single request, while the generous output ceiling allows for lengthy generated code, detailed plans, or extended reasoning traces without truncation. Because it handles images and files alongside text, you can mix formats in one request rather than preprocessing everything into plain text first.

Those capabilities point to a fairly specific set of use cases. Complex software engineering tasks fit well: reading across a large codebase, tracing how a change propagates, and producing a substantial patch. Agentic workflows are another fit, where the model plans steps, calls tools, and reacts to results over many turns. Long-horizon computer use, as Anthropic describes it, means sustained interaction with an environment rather than a single question and answer. Multimodal input extends this to tasks where screenshots, diagrams, or documents need to be read together with text, such as reviewing a design mockup against an implementation or extracting structure from a scanned file.

You call it through api14's OpenAI-compatible API using the model id anthropic/claude-opus-4.5, so existing OpenAI SDK code works with a changed base URL and model string. The supported request parameters are include_reasoning, max_completion_tokens, max_tokens, reasoning, response_format, stop, structured_outputs, temperature, tool_choice, tools, top_k, and verbosity. In practice, tools and tool_choice drive agentic loops, structured_outputs and response_format constrain the shape of the reply, reasoning and include_reasoning control the reasoning behavior, and max_tokens or max_completion_tokens bound generation up to the 64,000 token output ceiling.

There is no separate lightweight variant listed here, so the main consideration is whether you need this level of reasoning at all. For straightforward text tasks, a smaller model may be sufficient and faster. Also note that output is text only: the model reads images and files but does not generate them. If your task requires image or audio output, this model is not the right fit.