What is Claude Opus 4.1？
Claude Opus 4.1 is the latest large language model released by Anthropic, an upgraded version of Claude Opus 4. The model has been optimized and enhanced across multiple aspects, including reasoning quality, instruction-following ability, and overall performance. In safety evaluations, Claude Opus 4.1 performs excellently, with the harmless response rate rejecting inappropriate requests increasing from 97.27% to 98.76%, while maintaining a very low rejection rate for benign requests on sensitive topics, comparable to Claude Opus 4. The model excels in programming, writing, tool usage, and agent capabilities, achieving the highest score of 74.5% on the SWE-bench programming leaderboard.
Main Features of Claude Opus 4.1
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Advanced Programming Capabilities: Efficiently handles complex programming tasks, supports up to 32k tokens in a single output, and generates high-quality, context-aware code adaptable to different coding styles.
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Agent Abilities: Possesses strong autonomous decision-making skills, capable of precisely managing multi-channel marketing campaigns and coordinating complex enterprise workflows.
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Powerful Search: Independently completes research tasks lasting several hours, analyzing multi-source information from patent databases, academic papers, and market reports.
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Content Creation: Generates high-quality, natural, human-level text with outstanding performance in creative writing, producing stories with depth and rich characters.
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Hybrid Reasoning: Supports instant response and extended step-by-step reasoning, allowing users to choose the appropriate reasoning mode based on task requirements.
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Safety and Compliance: Demonstrates strong safety performance, reliably rejecting requests that violate usage policies.
Technical Principles of Claude Opus 4.1
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Transformer-Based Architecture: Built on the Transformer architecture, a neural network based on self-attention mechanisms that can handle long sequences and capture complex contextual relationships. Using multi-layer encoders and decoders, the model progressively extracts and generates high-quality text.
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Large-Scale Pretraining: Trained on massive text datasets to learn language syntax, semantics, and logical relationships via mainly unsupervised learning by predicting the next word in text sequences.
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Instruction Tuning: Fine-tuned with instruction data to better understand and execute user commands, improving performance in specific tasks such as programming and writing.
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Hybrid Reasoning Mechanism: Supports both immediate reasoning (fast response) and extended reasoning (step-by-step thought), with API users able to finely control inference budgets to optimize cost and performance.
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Safety and Alignment: Extensively tested through single- and multi-turn evaluations to reject malicious requests, avoid bias, and protect child safety. Reinforcement learning and safety training ensure the model’s behavior aligns with human values and usage policies.
Performance of Claude Opus 4.1
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Programming Ability: Achieved a 74.5% score on the SWE-bench Verified benchmark, a 2% improvement over Opus 4 and significantly better than Sonnet 3.7 (62.3%) and OpenAI’s GPT-4.1 (54.6%).
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Long-Term Task Handling: Excels at managing long-duration tasks, autonomously handling multi-channel marketing and coordinating cross-functional workflows, with outstanding performance on TAU-bench for complex multi-step tasks.
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Reasoning Ability: Leads most metrics in agentic coding and reasoning benchmarks, outperforming Opus 4, OpenAI o3, and Gemini 2.5 Pro.
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Harmless Response Rate: Reached 98.76% harmless response rate in single-turn tests, significantly improved from Opus 4’s 97.27%.
Project Links for Claude Opus 4.1
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Official Website: https://www.anthropic.com/claude/opus
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Technical Paper: https://assets.anthropic.com/m/4c024b86c698d3d4/original/Claude-4-1-System-Card.pdf
Pricing for Claude Opus 4.1
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Input Price: $15 per million tokens
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Output Price: $75 per million tokens
Application Scenarios of Claude Opus 4.1
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Software Development and Code Optimization: Generates high-quality code and supports multi-file code refactoring with outputs up to 32k tokens, significantly boosting development efficiency.
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Enterprise Automation Workflow Management: Autonomously manages multi-channel marketing and coordinates complex, long-duration workflows to improve operational efficiency.
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Market and Academic Research: Independently conducts research lasting several hours, analyzing multi-source data to provide comprehensive insights and strategic advice.
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Content Creation and Copywriting: Produces high-quality, natural, human-level text, excelling in creative writing to quickly generate articles, stories, and advertisements.
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Education and Learning Assistance: Serves as an educational tool providing personalized learning suggestions, answering questions, and generating learning materials to enhance teaching and learning experiences.
