Anthropic: Claude Sonnet 4.6

Claude Sonnet 4.6 is a text model from Anthropic, listed on api14 as anthropic/claude-sonnet-4.6. Anthropic describes it as its most capable Sonnet-class model yet, with frontier performance across coding, agents, and professional work. It sits in the text category and is aimed at developers building applications that need strong reasoning and generation quality rather than a lightweight, low-latency model.

The model accepts text, images, and files as input, and it produces text as output. Its context window is 1,000,000 tokens, which is large enough to hold entire codebases, long document sets, or extended conversation histories in a single request. The maximum output is 128,000 tokens, so responses can be long form when the task requires it, such as full file rewrites or detailed reports. Because images and files are accepted alongside text, you can mix prompts with screenshots, diagrams, PDFs, or source files without a separate preprocessing step.

Those capabilities point to a few practical uses. Iterative development and complex codebase navigation are natural fits: you can feed in a repository or a large slice of one and ask for changes, reviews, or explanations across many files. Agent-style workflows benefit from the tool calling support and the long context, since an agent can carry state and intermediate results without constant truncation. Professional work such as document analysis, summarisation, and structured extraction from mixed text and image inputs also follows from the input modalities and the structured output options. The reasoning controls let you trade depth against speed depending on the task.

You call the model through api14's OpenAI-compatible API using the model id "anthropic/claude-sonnet-4.6". The supported request parameters are include_reasoning, max_completion_tokens, max_tokens, reasoning, reasoning_effort, response_format, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p, and verbosity. In practice this means you can set sampling behaviour with temperature, top_p, and top_k, cap output length with max_tokens or max_completion_tokens, control reasoning depth with reasoning, reasoning_effort, and include_reasoning, and constrain the response shape with response_format and structured_outputs. Tool use is configured through tools and tool_choice, and verbosity adjusts how much the model says.

The main constraint to keep in mind is that output is text only, so tasks that need generated images or audio fall outside this model. The 128,000 token output cap also means very large single responses must be split or streamed in stages. If your workload is simple classification or short replies where latency and throughput matter more than reasoning depth, a smaller model may be a better fit. For long-context coding, agent loops, and mixed text, image, and file analysis, Claude Sonnet 4.6 is the stronger choice.