Claude Opus 4.7

• Agentic Programming Capability
Achieved a high score of 87.6% on authoritative programming benchmarks such as SWE-bench Verified. Excels in complex code reasoning, system design, and long-horizon autonomous execution. The model can independently handle the entire workflow from requirement understanding and solution design to code implementation and test validation, significantly reducing the need for human intervention.

• Knowledge Work and Professional Analysis
Delivers exceptional performance on specialized tasks such as financial analysis and document creation, generating more rigorous analytical reports and more professional presentations. The model possesses stronger logical reasoning capabilities and fact-based grounding, making it well-suited for complex knowledge-intensive tasks that require deep thinking and structured output.

• Visual Understanding Capability
Visual capabilities have been tripled compared to the previous generation, supporting high-resolution images with a long edge of up to 2576 pixels (approximately 3.75 megapixels). Accurately recognizes fine-grained visual content such as charts, dense documents, and UI screenshots, enabling complex information extraction and analysis in multimodal input scenarios.

• Long-term Memory and Context Processing
With a 1-million-token context window and enhanced cross-session memory capabilities, the model maintains stability and coherence throughout long-horizon, multi-step tasks. Well-suited for scenarios such as long-form document analysis and complex project progression across multi-turn conversations.

• Precise Instruction Following
Executes user instructions with greater precision, tending to interpret and follow them literally. This reduces ambiguous interpretation and over-extension, improving the controllability and reliability of output results.

• Self-Verification Mechanism
The model performs self-verification and checking before delivering final answers, actively identifying and correcting potential errors. This improves the quality and accuracy of first-pass outputs while reducing the need for later manual review.

• Adaptive Reasoning and Inference Intensity Control
The model employs an adaptive reasoning mechanism that dynamically adjusts thinking depth based on task difficulty. It also offers multiple inference intensity control options ranging from low to xhigh (ultra-high), allowing users to flexibly configure settings based on specific task requirements and cost budgets, striking a balance between performance and resource consumption.

• Code Review and Automation Assistance
For developer scenarios, Claude Code introduces dedicated code review commands and an auto mode, efficiently identifying potential issues, security vulnerabilities, and optimization opportunities within code, further boosting developer productivity.

Software Engineers and Development Teams

Handling "long-horizon delivery tasks" that require a high degree of autonomy, such as large-scale codebase refactoring, complex feature development, and in-depth code review. The model excels at driving these tasks forward independently, significantly reducing the need for continuous developer oversight.

Finance and Legal Professionals

Conducting complex financial analysis, multi-step research, and the creation and review of professional documents and presentations. The model's strong reasoning and fact-grounded capabilities make it well-suited for these high-stakes, detail-oriented domains.

AI Application Builders

Serving as the core engine for complex AI agents that require long-duration operation, multi-step planning, and extensive tool calling. The model provides the autonomous reasoning and execution backbone needed to power sophisticated agentic workflows.

Users with High Quality Requirements

Those who are willing to pay a premium for higher first-pass accuracy and fewer downstream corrections, thereby achieving significant time savings overall. For these users, the model's improved output quality and self-verification capabilities translate directly into reduced rework and faster project completion.