Complete Claude Opus 5.5 Guide

Claude Opus 5.5 is Anthropic’s newest Opus model, released on September 22, 2026.

Anthropic built it for demanding coding, AI agents, computer use, professional knowledge work, financial analysis, research, and complex multi-step tasks.

Anthropic calls it its strongest Opus model so far.

• Agentic coding
• Complex reasoning
• Long-running tasks
• Large codebase work
• AI agents
• Multi-tool workflows
• Computer use
• Professional knowledge work
• Financial analysis
• Document analysis
• Research and synthesis
• Visual understanding
• Spreadsheet, presentation, and document work
• Following detailed instructions
• Clear professional writing

• 20% cheaper input tokens
• 20% cheaper output tokens
• 60% cheaper cache reads
• About 40% lower cost on typical workloads at default settings, per Anthropic
• Over 30% faster output generation
• Better agentic coding performance
• Better computer-use capabilities
• Better knowledge-work performance
• Greater token efficiency
• Fewer steps on many agentic tasks
• Clearer, more natural communication
• Stronger results on long-running projects

Claude Opus 5.5 runs on adaptive thinking.

It decides how much reasoning each task needs instead of treating every request the same.

Medium is the default effort.

Higher effort can lift results on hard tasks, but it usually adds latency and uses more tokens.

Low

Use it for simple tasks.

Medium

Use it for most everyday professional work.

High or XHigh

Use it for hard coding, analysis, and agentic workflows.

Max

Save it for exceptionally hard tasks where quality beats cost and speed.

Opus 5.5 is especially strong at agentic software engineering.

• Build features
• Debug complex issues
• Refactor code
• Review pull requests
• Navigate large repositories
• Run terminal workflows
• Investigate root causes
• Write tests
• Fix failing tests
• Perform codebase-wide migrations
• Work across multiple files
• Explain its modifications
• Execute long-running engineering tasks

Opus 5.5 is built for workflows where Claude must do more than answer a single prompt.

• Plan multi-step tasks
• Use tools
• Coordinate subagents
• Navigate applications
• Gather information
• Analyze results
• Take sequential actions
• Check its own work
• Keep progress moving on long tasks
• Report what it completed
• Flag what it needs next

That makes it a strong fit for autonomous and semi-autonomous AI agents.

Opus 5.5 pairs visual understanding with reasoning for work done on a computer.

• Websites
• Software interfaces
• Screenshots
• Documents
• Charts
• Dashboards
• Diagrams
• Multiple applications

Agents built on the model can then run multi-step digital workflows that need both perception and judgment.

Opus 5.5 targets serious professional work, including:

• Market research
• Competitive analysis
• Strategy
• Financial research
• Data interpretation
• Document synthesis
• Business analysis
• Report generation
• Planning
• Decision support
• Due diligence
• Complex research

Anthropic reports an Elo score of 1,846 on GDPval-AA v2.1, a benchmark of real-world professional work across dozens of occupations.

• Reading financial filings
• Analyzing financial models
• Reviewing charts
• Comparing companies
• Extracting financial information
• Research synthesis
• Scenario analysis
• Creating financial summaries
• Supporting reporting workflows

Keep a human check on financial decisions and compliance-sensitive outputs.

Opus 5.5 can reason over visual inputs such as:

• Screenshots
• Charts
• Graphs
• Diagrams
• Documents
• Tables
• Interface layouts
• Complex imagery

Use it when text and visuals must be read together.

Opus 5.5 aims to communicate more naturally than earlier Opus models.

• Put important information first
• Follow writing constraints
• Produce clearer explanations
• Reduce unnecessary jargon
• Hold instructions across longer sessions
• Produce professional business writing

Use it for reports, documentation, research summaries, marketing material, proposals, strategy documents, and other professional writing.

It is at its best on large, sprawling projects.

• Repository-wide code migrations
• Large code audits
• Multi-stage research
• Complex business analysis
• Agent workflows that span many tools
• Large document collections
• End-to-end software projects

The value is not just answering hard questions. It is staying useful across many connected steps.

Opus 5.5 is a natural fit for Claude Code.

• Building applications
• Exploring unfamiliar repositories
• Debugging
• Refactoring
• Testing
• Code review
• Architecture analysis
• Large migrations
• Terminal automation
• Complex multi-file implementation

On hard engineering projects, give Claude a clear objective, constraints, testing requirements, and a definition of done.

Model ID for the API:

claude-opus-5-5

Input

$4 for every 1 million tokens

Output

$20 for every 1 million tokens

Cache reads

$0.20 for every 1 million tokens

5-minute cache writes

$5 for every 1 million tokens

1-hour cache writes

$8 for every 1 million tokens

Prompt caching can cut costs sharply when workloads reuse the same large context.

Opus 5.5 also offers Fast Mode for supported Claude Code and Claude Platform usage. Anthropic says it can generate up to roughly 2.5x faster.

Input

$8 for every 1 million tokens

Output

$40 for every 1 million tokens

Choose Fast Mode when latency matters more than keeping token costs low.

• Claude
• Claude Code
• Claude Platform
• Claude API
• Amazon Web Services
• Google Cloud
• Microsoft Foundry

In Claude, Opus 5.5 is open to eligible Pro, Max, Team, and Enterprise users.

1. Role

State the expertise or perspective the task needs.

2. Objective

Describe the final result you need in plain terms.

3. Context

Give Claude the background it needs to understand the situation.

4. Input

Hand over the documents, data, code, requirements, or other source material.

5. Constraints

Spell out what Claude must and must not do.

6. Process

For complex workflows, outline the key stages or requirements.

7. Output

Define exactly what the finished answer must contain.

8. Quality bar

Describe what a successful result looks like.

Role: Act as [role/expertise]. Objective: Complete [specific objective]. Context: Relevant background: [context]. Input: Work from this information: [input]. Requirements: Meet these requirements: [requirements]. Constraints: Do not [constraints]. Process: Think the task through, use available tools where they help, verify key assumptions, and check your work before you finish. Output: Return the result as [desired format]. Success criteria: The result must be [accuracy, completeness, tone, depth, or other quality requirements].

• Give Claude one specific goal
• Provide relevant context
• Define success clearly
• Keep instructions apart from source material
• Specify the output structure you want
• Add examples when formatting matters
• Break complex prompts into structured sections
• Supply tools when the task needs external actions
• State what Claude may change
• Set boundaries for autonomous tasks
• Ask Claude to verify finished work
• Cache repeated large contexts
• Match effort to task difficulty
• Skip Max effort when a lower setting already solves the task

• Writing
• Summarization
• Research synthesis
• Everyday coding
• Document analysis
• Business analysis
• General professional work

• Complex debugging
• Difficult coding
• Architecture decisions
• Large codebase changes
• Deep research
• Complex financial analysis
• Multi-step agents
• Difficult reasoning
• Computer-use agents

• Extremely difficult reasoning
• Hard engineering problems
• High-complexity agentic tasks
• Problems where extra reasoning justifies the added cost and latency

Developers

Coding, debugging, refactoring, migrations, code review, architecture, and autonomous engineering work.

Businesses

Research, analysis, reports, documents, spreadsheets, presentations, workflows, and agent building.

Researchers

Literature analysis, synthesis, data interpretation, research planning, and hard reasoning.

Analysts

Financial analysis, market research, competitive intelligence, document analysis, and reporting work.

Creators

Research, ideation, scripting, editing, content repurposing, and structured knowledge extraction work.

AI Builders

Tool-using agents, coding agents, browser agents, enterprise workflows, computer-use systems, and multi-agent setups.

Opus 5.5 is often overkill for simple, high-volume tasks.

For basic classification, short extraction, simple rewrites, plain summaries, or huge repetitive workloads, a smaller, cheaper model may offer better economics.

Reach for Opus 5.5 when its extra intelligence, reasoning, reliability, coding skill, or agentic performance adds real value.

• It can still get facts wrong
• Generated code still needs testing
• Research claims still need verification
• Tool-using agents can take the wrong action
• More reasoning does not guarantee a correct answer
• Higher effort raises compute cost
• Benchmark wins do not guarantee an edge on every real-world task
• Sensitive financial, legal, medical, security, and other high-stakes work still needs proper human review