claude-opus-4-1-20250805（Claude Code）

Basic Information

Claude Opus 4.1 is Anthropic’s flagship AI model officially released on August 5, 2025. It is built upon and comprehensively upgraded from Claude Opus 4, while retaining Anthropic’s self-developed core architectural framework. As a key iterative version of the Claude series, its model API ID is claude-opus-4-1-20250805. The model focuses on enhancing programming capabilities and upgrading agent usability, representing Anthropic’s core technological achievements in production-level AI programming. Ordinary users can access it through Claude’s paid services and the Claude Code platform, while developers can call it via the Claude Developer Platform, Amazon Bedrock, and Google Cloud’s Vertex AI. API users only need to replace the model tag for seamless migration and integration.

Core Features

Outstanding Programming Capabilities: Production-Level Bug Fixing and Full-Process Development Coverage

As an official key upgrade focus, Claude Opus 4.1 excels in the SWE-bench Verified benchmark—a test that measures real-world software engineering capabilities—achieving an accuracy rate of 74.5%, a 2-percentage-point improvement over the previous Opus 4’s 72.5%. This significantly outperforms competitors such as GPT-4o (by 5.4 percentage points). The test is based on real GitHub issues, meaning the model can independently resolve about three-quarters of actual code vulnerability problems. In the Terminal-Bench specialized test for terminal operations, the model scored 43.3%, a notable improvement over the previous version’s 39.2%, reflecting enhanced practicality in real development environments, including terminal interaction and command execution scenarios. The model covers the entire development workflow—from implementing complex algorithms and refactoring large-scale projects to locating and fixing bugs—and can quickly retrieve and integrate internal documentation and external database information to generate comprehensive analysis reports, making it well-suited for enterprise-level software development scenarios.

Agent Tool Usage: Enhanced Practical Scenario Adaptation

The model demonstrates optimization results closely aligned with real-world scenarios in the area of agent tool usage (Agentic tool use). In the Retail scenario TAU-bench test, it achieved a score of 82.4%, up 1 percentage point from the previous generation; in the Airline scenario, it scored 56.0%, which is slightly lower than the previous version, but the official explanation attributes this to an optimized testing methodology—adding a requirement for additional thinking steps and increasing the maximum number of operational steps from 30 to 100, better matching the multi-step decision-making needs in real-world scenarios and emphasizing “precise task completion” over “rapid output.” This optimization makes the model more stable in complex tasks requiring multiple rounds of reasoning and cross-tool collaboration. Combined with Anthropic’s existing agent development ecosystem, it lowers the development threshold for enterprise-level agents in scenarios such as service scheduling and task decomposition.

Computer Operations and Tool Interaction Capabilities: Specialized Enhancement for Terminal Scenarios

Benefiting from the significant improvement in the Terminal-Bench test, the model’s practicality in core development scenarios such as terminal operations and command-line interactions has greatly increased. It can autonomously perform computer operations at the development end, including code compilation and execution, project environment configuration, and log analysis. Although OSWorld and other general computer task test data have not been disclosed, judging from its performance in the terminal-specific tests, it shows collaborative capabilities in interacting with office software and professional development tools related to development, making it adaptable to integrated development and operations, automated testing, and other similar scenarios.

Inference and Knowledge Processing Capabilities: Focused on Practical Application

In inference capability tests, the model exhibits a clear practical orientation. While its performance in the GPQA Diamond (graduate-level inference) test falls short of competitors (GPT-4o at 83.3% and Gemini 2.5 Pro at 86.4%), no specific scores have been made public. However, in the AIME 2025 (high school math competition) test, it scored 78%, lower than GPT-4o and Gemini 2.5 Pro’s 88%+, reflecting Anthropic’s product strategy of focusing on practical scenarios rather than theoretical score-chasing. In professional applications, thanks to its strong code comprehension and data integration abilities, the model demonstrates excellent logical reasoning and knowledge application skills in scenarios such as generating technical documentation related to software development, interface design, and performance optimization recommendations, making it an efficient auxiliary tool in technology R&D. Currently, publicly available channels have not disclosed its results in general inference tests like multilingual question answering (MMMLU).

Multi-modal and Contextual Advantages: Suitable for Large-Scale Development Tasks

Currently available information does not explicitly mention any new capabilities of the model in multi-modal input and output. It is speculated that the model continues the text-based interaction approach of the previous generation and possesses robust multilingual processing capabilities to meet the needs of global development teams. Regarding the context window size and the maximum number of tokens per output, the official has not yet released specific data. However, considering its ability to handle large-scale project development, it is expected to meet the demands of long-form code document generation, collaborative development across multiple modules, and large-scale project documentation integration.

Technical Highlights

Developer Tool Ecosystem: Seamless Migration and Efficient Collaboration

The model is deeply integrated into the Claude Code development ecosystem. Although it has not been disclosed whether it includes new features such as Claude Code v2 and “checkpoints,” its seamless migration design via API interfaces reduces developers’ upgrade costs and allows quick integration into existing development workflows. Combined with the improvements in the Terminal-Bench test, the model’s direct interaction capabilities in dialogue scenarios—such as executing code and creating files—have been strengthened. It is speculated that the model continues its native development tool adaptation capabilities and can work seamlessly with mainstream development environments to enhance development efficiency.

Security Framework: Continuing High-Level Security Standards

Currently available channels have not explicitly disclosed the security release framework level or specific security optimization measures adopted by the model. However, as Anthropic’s flagship model, it is speculated to continue the high-precision content filtering mechanism of previous products, effectively blocking high-risk content while also possessing strong resistance against prompt injection attacks, meeting the security compliance requirements of enterprise-level development scenarios. The official has not mentioned the application of interpretability technologies.

Cost and Deployment Optimization: Flexible Deployment and Stable Pricing

The model supports flexible deployment across multiple platforms, adapting to the technical architecture needs of enterprises of different sizes. In terms of pricing, the API service maintains stable standards: the input price is $15 per million tokens, and the output price is $75 per million tokens. Users are also advised to make full use of optimization features such as prompt caching to reduce usage costs. For simple tasks, they can combine it with other lightweight versions of the Claude model to achieve cost balance.

Market Impact

The release of Claude Opus 4.1 is seen by the industry as an important technological move in the AI programming field ahead of the GPT-5 launch. With a score of 74.5% in the SWE-bench Verified test, it breaks the industry record, marking further maturity in AI’s application in real-world production-level programming scenarios. The model’s three core capabilities—“high-accuracy programming + practical agents + enhanced terminal interaction”—make it the preferred choice for enterprise-level software development, integrated DevOps, and complex project refactoring. Thanks to its leading performance in real-world development scenarios, the model is particularly favored by industries such as high-end software R&D, internet technology services, and fintech, which have high demands for programming accuracy and development efficiency. It is expected to drive innovation in developer team productivity and lay a market foundation for Anthropic in the upcoming competition for next-generation AI models. The official also revealed that more substantial model upgrades will be rolled out gradually “in the coming weeks,” further strengthening its market competitiveness.

Just change the API Base in Claude Code to:https://api.302.ai/cc or https://api.302ai.cn/cc and use the key directly created in the backend as the APIKey.

With the official API charging at 70% of the original rate, you’ll need to change the Base Url in Claude Code.