Anthropic: Claude Opus 5.5

Claude Opus 5.5 is Anthropic's flagship model, succeeding Claude Opus 5, and it is positioned for demanding reasoning, coding, and long-horizon agentic work. Anthropic is the provider behind the Claude family, and this release is their top tier for tasks where accuracy and sustained focus matter more than raw speed. It was released on 22 September 2026.

The model accepts text, images, and files as input, and returns text. Its context window is 1,000,000 tokens, which means you can send very large documents, whole repositories worth of source files, or long conversation histories in a single request without chunking. The maximum output is 128,000 tokens, so responses can be long, including full file rewrites, multi-section reports, or extended reasoning traces. Because files and images are both supported as inputs alongside text, the model can work over mixed material such as a screenshot plus a specification document, or a PDF plus a relevant code excerpt.

Those capabilities point to several practical uses. Multi-step changes in large codebases are a natural fit: with a million token context you can supply the surrounding modules and let the model plan and apply coordinated edits. Long-horizon agentic work suits the model too, since it supports tools and tool choice, meaning it can call functions or external services across many turns. Document and file understanding, such as extracting structured data from uploaded records or summarising long reports, follows from its file input support. Reasoning-heavy tasks, from analysis to planning, are covered by the reasoning parameters it exposes.

You call the model through api14's OpenAI-compatible API using the model id "anthropic/claude-opus-5.5" in the same way you would call any other chat completion endpoint. Supported request parameters are include_reasoning, max_completion_tokens, max_tokens, reasoning, reasoning_effort, response_format, stop, structured_outputs, tool_choice, tools, and verbosity. Structured outputs and response_format are useful when you need the model's answer in a specific shape, while tools and tool_choice let you wire it into an agent loop. If you want to control how much internal reasoning it produces, reasoning and reasoning_effort are the parameters to adjust, and verbosity lets you steer response length independently of the output token cap.

Consider whether a smaller or cheaper sibling model fits better when your task is short and latency-sensitive, since a flagship model with a million token context and 128,000 output tokens is built for the harder end of the workload. For straightforward text generation, classification, or simple tool calls, a lighter model from the same provider may be sufficient.