Claude Opus 5

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.

How to Use Claude Opus 5

Claude Opus 5 is Anthropic's Opus-tier model for serious coding, agents, and knowledge work. You call it with a single prompt and get Claude's full response back synchronously. Good results come mostly from writing concrete, verifiable tasks and choosing the right effort level.

Writing effective prompts

State the task, the inputs, and what a correct answer looks like. For coding, paste the code and ask for the root cause, a corrected version, and a test. For analysis, name the format you want — a table, a JSON object, a short summary. Add a system instruction to lock in role, tone, or output rules across the whole request. To ask about a picture, attach an image (PNG, JPEG, WebP, or GIF).

Recommended Settings

• •effort: low for chat, extraction, and classification; medium as the coding sweet spot; high or xhigh for complex refactoring, agentic coding, and multi-step analysis; max only when correctness matters more than latency. Higher effort spends more thinking tokens and can overthink simple tasks.
• •response_format: pass a JSON schema (list every property in required, set additionalProperties to false) when you need machine-readable output. json_object is not supported.
• •Context: up to 1M tokens — paste whole files, repositories, or document sets and ask for a synthesis.

In our own testing at high effort, Opus 5 took a short Python function with several planted bugs and diagnosed each one, gave a one-line root cause, returned a corrected function, and wrote a unit-test suite whose failing tests mapped to each bug. Expect this kind of self-checking on well-scoped coding tasks; for simpler generation, drop to medium to save tokens with little quality loss.

Prompts to try

• •"Review this function for correctness and edge cases, then return a fixed version with tests."
• •"Refactor this module into smaller units and resolve the circular import."
• •"Compare these three annual reports and produce a table of revenue, margin, and key risks."
• •"Read this stack trace and the attached file, then find the root cause and propose a patch."

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.