Claude Sonnet 5

Agentic coding, debugging and reasoning with 1M-token context.

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Claude Sonnet 5 — Text Generation (LLM)

What is Claude Sonnet 5?

Claude Sonnet 5 is Anthropic's most agentic Sonnet-class model, built for coding, reasoning, and everyday professional work. It is a drop-in upgrade for Sonnet 4.6 that narrows the gap to Opus-class models, bringing performance that recently required larger models to a fast, mid-tier model. On Segmind it runs as a synchronous text endpoint that also accepts an optional image for multimodal analysis, so you get a direct JSON response with no polling. Adaptive thinking is on by default, and a single effort control lets you dial reasoning depth from low to max. The model ships a 1M-token context window; on Segmind each response can return up to 64K output tokens.

Key Features

  • •Agentic coding across the software lifecycle: planning, implementation, debugging, maintenance, and large-scale refactors.
  • •Root-cause debugging that returns a corrected function plus tests, often checking its own output.
  • •Adaptive thinking with fine-grained effort levels: low, medium, high, xhigh, max.
  • •1M-token context window and up to 64K output tokens per response for long files and long conversations.
  • •Structured outputs via response_format using a JSON Schema for machine-readable results.
  • •Optional image input for multimodal questions (PNG, JPEG, WebP, GIF).

Best Use Cases

Reach for Claude Sonnet 5 when you need reliable coding help: fixing bugs in existing (brownfield) code, tracing a failure to its real cause, writing reproducing tests, or refactoring across a large file. In testing, given a buggy interval-merge function at effort: high, it named both root causes, returned a corrected implementation, and wrote pytest cases that fail on the original and pass on the fix. It is equally strong on multi-step reasoning, data extraction and classification, structured JSON output for downstream systems, and knowledge work such as research synthesis and drafting. The optional image input suits chart reading, document analysis, and screenshot Q&A.

Prompt Tips and Output Quality

State the task and the shape of the answer you want. For coding and multi-step problems, raise effort to high or xhigh; keep it low for chat, extraction, and classification. Use instruction to set a persona or output rules, and response_format when you need strict JSON. temperature is not supported on this model: the API ignores it and returns an X-Warning header, so leave it out. Include concrete inputs (code, data, constraints) so the model can verify its own work.

FAQs

Is Claude Sonnet 5 good for coding? Yes — coding and agentic tasks are its largest gains over Sonnet 4.6, including debugging and refactors.

How do I control reasoning depth? Set effort: low for quick answers, high/xhigh for coding, max when correctness matters most.

Can it return structured JSON? Yes, pass a JSON Schema in response_format; json_object mode is not supported.

Does it accept images? Yes, one optional image (PNG, JPEG, WebP, GIF) for multimodal analysis; video is not supported.

What is the context window? 1M tokens, with up to 64K output tokens per response on Segmind.

Is it a drop-in for Sonnet 4.6? Yes — swap the model string and drop any temperature value (it is ignored with a warning here).