Claude Opus 5.5

Claude Opus 5.5 — Text Generation (LLM)

What is Claude Opus 5.5?

Claude Opus 5.5 is Anthropic's Opus-tier flagship large language model and the first model in the Claude 5.5 family. This hybrid reasoning model is built for serious coding, long-running AI agents, and professional knowledge work, pairing frontier reasoning with adaptive thinking and a 1M-token context window. Anthropic positions it as a major step up from Opus 5 that performs at the level of Claude Fable 5.1 on most work, with clearer, less verbose answers. On Segmind you call claude-opus-5.5 through a single synchronous endpoint that returns Claude's full response.

Key Features

• •Frontier agentic coding and reasoning: production-ready code, large-codebase refactors, and leading agentic-coding scores.
• •Finds and fixes software inefficiencies: codebase-wide migrations and audits, subtle-bug review, and self-checking debugging.
• •Adaptive thinking via 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 work.
• •Optional image input, a system instruction, and JSON-schema structured output.

Best Use Cases

Reach for Opus 5.5 on high-value work where a wrong answer costs more than the reasoning to get it right: multi-file refactors, large codebase migrations and audits, resolving real GitHub issues, code review, and debugging with generated tests. It powers production agents that run autonomously across long horizons, verifying their own work, and is equally strong on knowledge work — financial modeling, table reasoning, legal redlines, and 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, medium as the coding sweet spot, high or xhigh for complex refactoring, 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, returned a corrected version, and wrote unit tests that passed on the fix.

How to Use Claude Opus 5.5

Claude Opus 5.5 is Anthropic's Opus-tier model for serious coding, agents, and knowledge work, and the first model in the Claude 5.5 family. 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 5.5 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."
• •"Port this file from C to Rust and keep the behavior identical."
• •"Compare these three annual reports and produce a table of revenue, margin, and key risks."

When should I use Claude Opus 5.5?

For your most demanding coding, agent, and complex-document tasks.

How is Opus 5.5 better than Opus 5?

A step up in coding and agentic performance, matching Fable 5.1 on most work, with clearer answers.

Is Opus 5.5 multimodal?

Yes, it accepts an optional image; video input is not supported.

Does higher effort always help?

No — higher effort can overthink simple tasks; medium is often the coding sweet spot.

Can it return structured JSON?

Yes, via a response_format with a JSON schema; json_object is not supported.