Claude Opus 5

Frontier coding, debugging, and agents with 1M-token context.

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Claude Opus 5 — Text Generation (LLM)

What is Claude Opus 5?

Claude Opus 5 is Anthropic's Opus-tier flagship large language model, a hybrid reasoning model built for serious coding and AI agents. It pairs frontier reasoning and coding with adaptive thinking and a 1M-token context window, making it a step-change upgrade over Opus 4.8 for long-running agents, software engineering, and professional knowledge work. On Segmind you call claude-opus-5 through a single synchronous endpoint that returns Claude's full response, including its reasoning.

Key Features

  • •Frontier coding and reasoning: writes production-ready code, refactors large codebases, and leads agentic-coding evaluations.
  • •Adaptive thinking through an effort setting (low, medium, high, xhigh, max) that trades reasoning depth for speed.
  • •1M-token context window with strong long-context recall for whole-repository and multi-document work.
  • •Debugging and root-cause analysis, with the model checking its own work before it answers.
  • •Optional multimodal image input, a system instruction, and JSON-schema structured output.

Best Use Cases

Reach for Opus 5 on high-value, ambiguous work where a wrong answer costs more than the reasoning it takes to get right: multi-file refactors, resolving real GitHub issues, code review of subtle bugs, and debugging. It powers production agents that orchestrate multi-tool tasks and run autonomously across long horizons, verifying their own work. It is equally strong on knowledge work — financial modeling and table reasoning, legal redlines, scientific analysis, and enterprise document, spreadsheet, and slide creation.

Prompt Tips and Output Quality

Give concrete, verifiable tasks. For code, include the snippet and ask for the root cause, a fix, and a test. Set effort to low for chat and extraction, high or xhigh for coding and multi-step analysis, and max only when correctness outweighs everything. In our testing, a debugging prompt at high effort found every planted bug in a Python function, explained each root cause, and wrote a unit test that passed on the fix.

FAQs

When should I use Claude Opus 5? For your most demanding coding, agent, and complex document tasks that need frontier intelligence.

Is Opus 5 multimodal? Yes, it accepts an optional image for analysis; video input is not supported.

Does higher effort always help? No. Higher effort reasons longer and can overthink simple tasks; medium is often the coding sweet spot.

What context window does it support? Up to 1M tokens, enough for entire repositories or large document sets.

Can it return structured JSON? Yes. Pass a response_format with a JSON schema; json_object is not supported on Claude.