Claude 4.1 Opus
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About
Claude 4.1 Opus is a next-generation enterprise AI assistant built for complex, sustained workflows. It combines reliable long-term memory, flexible reasoning modes, and industry-leading coding capabilities to help teams and professionals tackle multi-step projects that span days, weeks, or months. Use it to manage cross-functional campaigns, run agentic searches across documents and the web, debug and refactor large codebases, or provide ongoing personalized coaching and customer support that remembers prior interactions.
Practical benefits include: maintaining context across long conversations and projects (supporting up to 32,000-token contexts), switching between fast summaries and detailed step-by-step reasoning, and autonomously orchestrating tools and APIs to complete multi-stage tasks. Developers will value its high coding accuracy (strong benchmark performance and improved junior-developer results), multi-file refactoring, and ability to output very long, coherent code and documentation. Product and marketing teams can rely on its strategic planning, error reduction, and ability to execute multi-channel workflows.
Safety and alignment are core features: Claude 4.1 Opus applies constitutional AI principles to reduce biased, harmful, or misleading outputs and deliberately defers to human experts in sensitive domains like medical or legal advice. It is accessible via API and major cloud platforms for enterprise integration.
Limitations: it is intentionally cautious in sensitive areas, requires thoughtful prompt design to unlock advanced agentic behavior, and may be heavier than necessary for trivial tasks. Overall, Claude 4.1 Opus excels where long-term context retention, high-accuracy coding, autonomous multi-step task execution, and safe, professional outputs are essential.
Percs
Large context
High accuracy
Agentic tools
Safe alignment
Support file upload
Settings
Temperature- The temperature of the model. Higher values make the model more creative and lower values make it more focused.
Response length- The maximum number of tokens to generate in the output.
Context length- The maximum number of tokens to use as input for a model.
Reasoning- Ability to think deeper
Reasoning Tokens- Budget for reasoning. Must be lesser than Max Tokens (length of an output)