Anthropic released Claude Opus 4.5 on November 24, 2025, marking a significant leap in AI capabilities. The model is now the best in the world for coding, agents, and computer use - and the benchmarks back that claim.

State-of-the-Art Coding Performance

The headline number is 80.9% on SWE-bench Verified, outperforming Google’s Gemini 3 Pro and OpenAI’s GPT-5.1. This benchmark tests AI models on real-world software engineering tasks from GitHub issues, making it a meaningful measure of practical coding ability.

For developers using AI assistants, this translates to more reliable code generation, better understanding of complex codebases, and fewer iterations needed to get working solutions.

Technical Specifications

  • Context window: 200,000 tokens
  • Output limit: 64,000 tokens
  • Knowledge cutoff: March 2025

The context window matches Sonnet, giving you plenty of room to work with large files and complex prompts. The 64K output limit is particularly useful for generating substantial code or documentation in a single response.

Where Opus 4.5 Excels

Beyond coding, the model shows meaningful improvements in:

  • Vision: Better understanding of images, diagrams, and screenshots
  • Reasoning: More accurate logical deductions and problem-solving
  • Mathematics: Stronger performance on quantitative tasks
  • Everyday tasks: Deep research, working with spreadsheets and presentations

The combination of these capabilities makes Opus 4.5 particularly effective for agentic workflows - tasks where the AI needs to plan, execute, and verify multi-step operations.

New Integrations

Anthropic also announced Chrome and Excel integrations alongside the model release. These enable Claude to interact directly with web browsers and spreadsheets, expanding the range of tasks it can automate.

Availability

Claude Opus 4.5 is available through:

  • Claude.ai (Pro and Team plans)
  • The Anthropic API
  • Microsoft Azure via Foundry

Practical Implications

For software teams, Opus 4.5’s coding capabilities mean AI pair programming becomes genuinely useful for non-trivial tasks. The improved reasoning helps with architecture decisions and code review, while the extended output window handles larger refactoring jobs.

The model represents a meaningful step forward in making AI assistants reliable enough for production development work.