AI model by Anthropic
Claude Opus 5.5 for business
Anthropic's recommended starting point for most serious work: long-running agentic coding and knowledge work, with a 1M token context window.
- Reviewed on September 24, 2026
- Anthropic
Capability tiersRelative, not benchmarks
Key facts about Claude Opus 5.5
- Model ID at review
- claude-opus-5-5
- Provider
- Anthropic
- Main category
- Reasoning
- Open weights
- No, available as a hosted service
- Last reviewed
- September 24, 2026
Fit
Where it fits and where it does not
Good at
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Agents that work through long tasks with many tool calls.
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Writing, reviewing and refactoring code across large codebases.
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Reading long documents and answering with care and nuance.
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Structured outputs that match a JSON schema.
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Understanding images, scans and screenshots alongside text.
Not the right choice for
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Very high volumes of simple requests, where Haiku costs far less.
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Instant replies in live chat, where a faster model fits better.
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Audio or video input.
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Teams that need to self-host the model.
Use cases
Business use cases we would use it for
Contract and policy review
Operations agents
Coding assistants
Our notes
When we would choose it
Opus 5.5 was announced on September 22, 2026, and Anthropic now suggests it as the place to start for most workloads. It is built for long-running agentic coding and knowledge work, and it supports tool use and structured outputs. That makes it a strong default for the reasoning step of an AI agent or a document review assistant.
We would pair it with a cheaper model for routine steps. For example, Claude Haiku can sort incoming requests and extract fields, while Opus handles the cases that need judgment. In our own development work, models of this class help with planning, code and tests, always with an engineer reviewing the result. See AI-accelerated development.
Because it is new, we would test it on real examples before replacing a model that already works. If you are on an older Opus version, run both side by side on the same tasks and compare quality, speed and cost.
Before you commit
Things to check before you commit
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Cost at your volume
Opus sits above Sonnet in price. Estimate monthly cost from real request sizes before launch.
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Context use
A 1M token window is available, but sending less text is faster and cheaper. Retrieve only what each task needs.
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Data terms and region
Check the data terms for your account and where requests are processed.
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Legacy versions
Older Opus versions are marked legacy. Pin the version you tested and plan upgrades.
Alternatives
Models to compare it with
Claude Sonnet 5
Anthropic's balance of speed and intelligence: a strong everyday model for assistants, document work, tool calling and coding.
Claude Fable 5.1
The top tier of the Claude family, for the most demanding reasoning and long-horizon agent work, priced above Opus.
GPT-6 Sol
The middle model of the GPT-6 family, positioned for complex coding and agentic workflows at a mid price level.
Keep exploring
Solutions, services and guides
Related services
View all related services- AI agents AI that takes actions in your systems, such as qualifying leads or processing requests, with people checking the results.
- AI copilots AI helpers built into your product or internal tools that draft, summarize and suggest while people decide.
- LLM integration Add a large language model to software you already have, with the guardrails, costs and logging handled.
- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- API development Well-documented APIs that let your product talk to apps, partners and AI tools reliably.
- Document automation Read invoices, forms, contracts and IDs, pull out the right fields and route them for review.
Solutions
View all solutions- Contract review assistant Highlight unusual clauses, missing terms and deviations from your standard positions, so reviewers focus where it matters.
- Knowledge assistant (RAG) Ask a question in plain words and get an answer from your own documents, with links to the sources.
- AI sales research agent Short, sourced briefings on each prospect before a call, drafted by an AI agent from public information and your CRM.
- AI-assisted content workflows Briefs, first drafts, edits and repurposing in a workflow where people set the angle and approve every word.
- AI lead qualification Score, research and route every new inquiry so your team talks to the best leads first.
- Automated quoting Draft accurate quotes and proposals from a request, your price rules and past work, for a person to approve.
Industries
View all industries- Legal Contract review assistants, knowledge search, intake and document automation for law firms and in-house legal teams.
- Real estate Listing sites, lead handling, CRM automation and document workflows for agencies, brokers and developers.
- Professional services Client portals, proposal drafting, knowledge assistants and internal tools for consultancies, agencies and firms.
- Education and elearning Course platforms, student portals, content workflows and AI tutoring assistants for schools and training companies.
- SaaS and startups MVPs, subscription billing, AI features, multi-tenant platforms and scaling support for founders and product teams.
- Marketing agencies Landing pages, content workflows, client reporting and white-label development capacity for marketing agencies.
Guides and articles
View all guides and articles- What are AI agents and how do businesses use them? What AI agents are, real business uses, the controls they need and how to start with one safely.
- How to choose an AI model for your business A step-by-step way to pick an AI model by testing candidates on your own tasks, data rules and budget.
- What is RAG and when do you need it? How retrieval-augmented generation lets AI answer from your documents, when it fits and how to build it well.
- Prompt writing basics for business teams How to write prompts that get consistent, useful results from AI tools, with templates your team can reuse.
- What is the Model Context Protocol? A business guide to MCP: what it is, how MCP servers work, who supports it and how to start using it.
- How to build an AI workflow with MCP servers Plan, connect and test an AI workflow that uses MCP servers, from a single task to a reliable process.
MCP servers
View all mcp servers- Apify Apify's official MCP server lets AI find and run ready-made scrapers and automation tools, called Actors, and read their results.
- Brave Search Brave's official MCP server gives AI live web, news, image, video and local search from Brave's own independent index.
- Browserbase Browserbase's hosted MCP server gives AI a cloud browser that can browse, act on pages, observe elements and extract data.
- Exa Exa's official hosted MCP server gives AI search built for AI use, page fetching, advanced filters and a research agent.
- Fetch The MCP project's reference Fetch server lets AI download a web page and read it as clean text or markdown.
- Firecrawl Firecrawl's official MCP server lets AI scrape, map, crawl and search the web and get clean, AI-ready content back.
Glossary terms
View all glossary terms- AI agent An AI agent is software that uses a language model to plan steps, call tools and act toward a goal, instead of only answering one question.
- Context window A context window is the maximum amount of text, measured in tokens, that an AI model can consider at once, including the question, documents and its answer.
- Model Context Protocol The Model Context Protocol, or MCP, is an open standard for connecting AI assistants to external tools and data in a consistent way.
- AI copilot An AI copilot is an assistant built into a tool people already use, which suggests, drafts and explains while the person stays in control.
- Function calling Function calling is a model feature that lets an AI model request a specific function, with structured inputs, for the application to run.
- MCP server An MCP server is a program or hosted service that exposes a system's data and actions as tools that AI assistants can use through the Model Context Protocol.
FAQ
Questions people ask us
Have a question that is not here? Ask us directly.
Long, complex tasks: agents with many steps, coding across large projects and careful work with long documents.
Anthropic lists a 1M token context window. Check the provider documentation for current output limits.
Yes. It sits above Claude Sonnet in price. Many systems use Sonnet or Haiku for most steps and Opus only where needed.