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Claude Opus 4.5

By Anthropic

IntermediateModel9.8K learners

5 is Anthropic's most capable large language model in the Claude 4 family, designed for the highest-difficulty reasoning, coding, and agentic tasks where maximum quality matters more than speed or cost.

Definition

Claude Opus 4.5 is Anthropic's most capable large language model in the Claude 4 family, designed for the highest-difficulty reasoning, coding, and agentic tasks where maximum quality matters more than speed or cost.

Overview

Claude Opus 4.5 sits at the top of Anthropic's model lineup, above the faster and cheaper Claude Sonnet and Claude Haiku tiers, and represents an incremental but meaningful upgrade over earlier Opus 4 releases in reasoning depth, coding accuracy, and reliability on long, multi-step agentic tasks. Like other Claude 4-generation models, Opus 4.5 supports extended thinking, where the model can allocate additional internal reasoning steps before producing a final answer, improving performance on complex math, coding, and multi-hop reasoning problems. Opus 4.5 is positioned specifically for workloads where task difficulty or the cost of an error is high enough to justify its higher price and slower throughput relative to Sonnet — for example, deep codebase refactors, complex agentic workflows involving many tool calls, or research-style analysis requiring sustained reasoning across a long context window. It supports the same core capabilities as the rest of the Claude 4 family, including a large context window, vision input, tool use and function calling, and computer use for controlling graphical interfaces, but with generally stronger performance on the hardest benchmark tasks in each category. As with prior Claude releases, Opus 4.5 is available through the Claude API, Claude.ai, and via major cloud marketplaces such as Amazon Bedrock and Google Cloud Vertex AI, and Anthropic pairs each release with a public model card detailing safety evaluations and a system card documenting known limitations, consistent with the company's stated approach to responsible scaling.

Key Features

  • Anthropic's highest-capability model tier within the Claude 4 family
  • Extended thinking mode for deeper reasoning on hard problems
  • Strong performance on complex coding, math, and multi-step agentic tasks
  • Large context window supporting long documents and extended conversations
  • Vision input for interpreting images, charts, and screenshots
  • Tool use, function calling, and computer use for controlling software interfaces
  • Available via Claude API, Claude.ai, Amazon Bedrock, and Google Cloud Vertex AI
  • Higher cost and lower throughput than Sonnet or Haiku tiers, reserved for hardest tasks

Use Cases

Complex, multi-file codebase refactors and large software engineering tasks
Long-running agentic workflows involving many sequential tool calls
Deep research and analysis tasks requiring sustained multi-step reasoning
High-stakes tasks where output quality matters more than response latency or cost
Advanced mathematical or scientific problem solving
Automating complex computer-use workflows via screen interaction

Alternatives

GPT-5 · OpenAIGemini · Google DeepMindGrok 4 · xAI

Frequently Asked Questions

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Frequently Asked Questions

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What is the SkillVeris tech glossary and how big is it?
The SkillVeris glossary is a free reference of roughly 2,000-plus technology terms, each with a clear plain-language definition. It spans AI, programming, web, DevOps, cloud, security and database vocabulary, so whenever a lesson, article or job description uses jargon you do not recognise, the glossary gives you a fast, reliable answer.
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How is the SkillVeris glossary different from Wikipedia?
The glossary is purpose-built for learners: definitions are short, plain-language and answer-first, sized for a quick lookup mid-lesson rather than a deep encyclopedic read. Entries also cross-link to related SkillVeris study notes, blog posts and courses, so a definition becomes a doorway into structured learning instead of a dead end.
Do blog articles use the Learn Through Hobbies method?
Many blog articles teach technical topics through hobby analogies, a hallmark of the SkillVeris blog, so you will find articles explaining programming through cricket, machine learning through music, or system design through cooking. The analogy is the teaching device; the article still delivers the real technical concept underneath.
Where can I find quick programming references while coding?
Open the SkillVeris cheat sheets, which are built exactly for that moment: compact, scannable references for syntax, commands and common patterns across languages and tools. Keep the relevant sheet in a browser tab while you work in Code Lab or your own editor, and dip into the glossary for terminology.
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The blog serves Indian learners plus a worldwide audience. Content stays globally relevant while acknowledging realities that matter in India, such as free access being essential for students and freshers, and career guidance that connects naturally to the SkillVeris jobs portal, which aggregates roles across India, UK, USA, Germany and Remote.
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SkillVeris content grows in response to what learners need, so feedback is welcome through the platform's support channels. If a term is missing from the glossary or a topic deserves an article, telling the team helps prioritise it. Meanwhile, the AI Mentor can answer the question immediately, 24/7, at any depth.
Do cheat sheets and glossary entries link to deeper learning?
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What makes SkillVeris programming references trustworthy?
The references are written to strict internal quality standards, kept consistent with the platform's 37 live courses, and never padded with invented statistics or hype. Definitions and cheat sheets are reviewed against the same content contracts that govern courses, and the answer-first style makes any inaccuracy easy to spot and correct.
How do the blog, glossary and cheat sheets fit into my learning routine?
Use them as satellites around your main course: read blog articles for context and motivation, hit the glossary the instant jargon appears, and keep cheat sheets open while coding. Together with study notes, Code Lab and the 24/7 AI Mentor, they turn passive reading into a complete, free learning system.

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