Anthropic: Claude Opus 5

Claude Opus 5 is Anthropic's flagship model, built for demanding reasoning, coding and long-horizon agentic work. It sits at the top of Anthropic's Claude line and is aimed at tasks where a smaller model would lose the thread or miss detail. It was released by the provider on 24 July 2026 and is listed on api14 under the text category.

The model accepts text, images and files as input, and returns text. Its context window is 1,000,000 tokens, with a maximum output of 128,000 tokens, so you can supply very large documents, repositories or image sets in a single request and still leave room for a long answer. That combination of a wide input surface and a large context makes it suited to work that involves reading a lot before writing anything.

Practical uses follow from those capabilities. Anthropic highlights end-to-end software tasks, code review and bug finding, and visual analysis. In day to day terms that means pointing the model at a codebase or a diff and asking it to locate defects, running multi step agents that need to hold a plan over many turns, and inspecting screenshots, diagrams or scanned files alongside the text that explains them. The large context is what allows whole repositories or document bundles to be passed in without aggressive chunking.

You call it through api14's OpenAI-compatible API using the model id "anthropic/claude-opus-5". The request parameters it supports are include_reasoning, max_completion_tokens, max_tokens, reasoning, reasoning_effort, response_format, stop, structured_outputs, tool_choice, tools and verbosity. The tools and tool_choice parameters fit agentic and function calling flows, structured_outputs and response_format let you constrain the shape of the reply, and the reasoning and reasoning_effort controls, together with include_reasoning and verbosity, let you tune how much deliberation the model does and how much of it is surfaced.

Because the model is Anthropic's flagship, it is the choice for tasks that need the extra capability rather than the default for every call. Simpler text-only work, or jobs where latency and throughput matter more than depth, may be better served by a lighter Anthropic model. The 128,000 token output ceiling also means a single response cannot exceed that, however much room the one million token input window leaves.