Anthropic: Claude Opus 4.8

Claude Opus 4.8 is a text generation model from Anthropic, listed in the Opus family and described by the provider as its most capable generally available model in that family. It is offered on api14 under the model id anthropic/claude-opus-4.8, so you can treat it as a drop-in option when you need Anthropic's strongest Opus-tier text model rather than a smaller or older sibling.

The model accepts text, images and files as input, and it returns text. Its context window is 1,000,000 tokens, which is large enough to hold very long documents, codebases or multi-turn histories in a single request, and its maximum output is 128,000 tokens. Reasoning is supported, so the model can work through problems before producing its final answer. Because input modalities include images and files alongside text, you can send mixed material such as a specification document plus a screenshot in one call and ask for a written result.

Those capabilities point to a fairly specific set of use cases. Long-context work is the obvious fit: reviewing lengthy contracts, summarising large research reports, or answering questions across a whole repository of documents. The image and file inputs suit tasks like extracting structured data from scanned forms, comparing a design mockup against written requirements, or turning a PDF plus a question into a written analysis. Reasoning support helps on problems that benefit from step-by-step deliberation, such as multi-constraint planning, debugging logic, or evaluating trade-offs in a technical decision. Text-only output keeps the result easy to pipe into downstream systems.

Calling it through api14 works like any other OpenAI-compatible chat completion: send a request to the chat completions endpoint with the model field set to anthropic/claude-opus-4.8. The supported 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 spends, request structured or JSON-shaped responses, define tools and control tool selection for function calling, and set stop sequences. The reasoning and verbosity parameters give you some control over how much internal deliberation and how much prose the model produces.

One practical consideration is that this is a text-output model, so if your application needs generated images, audio or embeddings, a different model will fit better. The 128,000-token output ceiling is generous but still finite, so extremely large single-response generations may need to be split across calls. For straightforward, high-volume tasks where deep reasoning and a 1M-token context are not needed, a lighter model may be the more sensible choice.