Claude Opus 4.1 - Anthropic's latest programming model

What is Claude Opus 4.1?

Claude Opus 4.1 is Anthropic's latest large-scale language model, an upgrade from Claude Opus 4. The model has been optimized and improved in several aspects, including inference quality, instruction compliance, and overall performance. In security assessments, Claude Opus 4.1 performed exceptionally well, increasing its harmless response rate for rejecting illegal requests 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 invocation, and proxy capabilities, achieving the highest score of 74.5% on the SWE-bench programming leaderboard.

Main functions of Claude Opus 4.1

• Advanced programming skillsIt supports efficient processing of complex programming tasks, supports single outputs up to 32k, generates high-quality, context-aware code, and adapts to different programming styles.
• Agent capabilitiesThe model possesses strong autonomous decision-making capabilities, enabling it to accurately manage multi-channel marketing activities and coordinate complex enterprise workflows.
• powerful search capabilitiesIt can independently complete research tasks that take several hours and simultaneously analyze information from multiple sources, including patent databases, academic papers, and market reports.
• Content creationIt can generate high-quality, natural and fluent human-level text, excels in creative writing, and can create stories with depth and rich characters.
• Mixed reasoning abilityIt supports instant response and extended stepwise reasoning, allowing users to choose the appropriate reasoning method based on task requirements.
• Security and complianceClaude Opus 4.1 performs well in terms of security, reliably rejecting requests that violate usage policies.

Technical principles of Claude Opus 4.1

• Transformer-based architectureClaude Opus 4.1 uses the Transformer architecture, a neural network architecture based on a self-attention mechanism, which can handle long sequences of data and capture complex contextual relationships. Based on multi-layer encoders and decoders, the model can progressively extract and generate high-quality text content.
• Large-scale pre-trainingThe model is pre-trained on massive amounts of text data to learn the syntax, semantics, and logical relationships of the language. The pre-training process mainly uses unsupervised learning methods, learning language patterns by predicting the next word in a text sequence.
• Command fine-tuningBased on instruction tuning, the model can better understand and execute user instructions. Fine-tuning for specific tasks (such as programming and writing) improves the model's performance in those areas.
• Hybrid reasoning mechanismThe model supports both immediate inference (rapid response) and extended inference (step-by-step thinking), allowing users to choose the appropriate inference method based on task requirements. The API provides users with fine-grained control over inference budgets, optimizing both cost and performance.
• Security and alignment mechanismsThe model's performance in rejecting malicious requests, avoiding bias, and protecting child safety was evaluated using extensive single-round and multi-round testing. Reinforcement learning and safety training were used to ensure that the model's behavior was consistent with human values and usage policies.

Claude Opus 4.1 performance

• Programming skillsIn the SWE-bench Verified benchmark test, Claude Opus 4.1 achieved a score of 74.5%, a 2 percentage point improvement over the previous version, Opus 4, and a larger improvement over Sonnet 3.7 (which only achieved 62.3%). This significantly outperforms OpenAI's GPT-4.1, which scored only 54.6%.
• Long-duration task processingClaude Opus 4.1 excels at handling long-running tasks, autonomously managing multi-channel marketing campaigns and coordinating cross-functional enterprise workflows. Its performance on TAU-bench is particularly outstanding, accurately handling complex, multi-step tasks.
• reasoning abilityIn benchmark tests of agentic coding and inference capabilities, Claude Opus 4.1 outperforms Opus 4 and other competing models, such as OpenAI o3 and Gemini 2.5 Pro, in most metrics.
• Harmless response rateIn a single round of testing, Claude Opus 4.1 achieved a harmless response rate of 98.76%, a significant improvement over Opus 4's 97.27%.

Claude Opus 4.1 project address

• Project official websitehttps://www.anthropic.com/claude/opus
• Technical Papers: https://assets.anthropic.com/m/4c024b86c698d3d4/original/Claude-4-1-System-Card.pdf

Claude Opus 4.1 product pricing

• Enter price$15 per million tokens
• Output Price$75 per million tokens

Application scenarios of Claude Opus 4.1

• Software development and code optimizationThe model can generate high-quality code, perform multi-file code refactoring, and support single outputs of up to 32k, significantly improving development efficiency.
• Enterprise Automated Process ManagementIt enables autonomous management of multi-channel marketing campaigns and coordination of cross-functional enterprise workflows, handling complex and time-consuming tasks, and improving enterprise operational efficiency.
• Market research and academic researchIndependently conduct research tasks lasting several hours, analyze multi-source information, provide comprehensive insights and strategic recommendations, and support market and academic research.
• Content creation and copywritingGenerates high-quality, natural, and fluent human-level text, excelling particularly in creative writing, and quickly generating articles, stories, and advertising copy.
• Education and learning supportAs an educational tool, it provides personalized learning suggestions, answers questions, and generates learning materials to enhance teaching effectiveness and learning experience.