Claude Opus 4.5 Sets New State-of-the-Art in Agentic Coding and Efficiency

Anthropic has introduced Claude Opus 4.5, marking the model as its most capable to date and setting a new industry standard for autonomous agents and complex computer use. This release focuses on balancing maximum capability with dramatic increases in token efficiency, making flagship performance both more reliable and more economical for real-world production workloads.

The defining feature of Opus 4.5 is its superior stability and resilience in long-horizon, autonomous tasks. While preceding models often struggled with multi-step reasoning, Opus 4.5 shows vastly improved performance on sustained, complex workflows—from large-scale code refactoring to troubleshooting multi-system bugs. This enhancement reveals a deeper stability and nuance in its reasoning. In one notable scenario involving an airline customer service simulation, Opus 4.5 provided a non-standard but legitimate solution to a complex request, a solution that the formalized test system failed to account for and initially marked as incorrect. This ability to creatively navigate ambiguity and solve problems outside of expected paths demonstrates a significant advancement in real-world application.

For developers, Opus 4.5 establishes a commanding new benchmark. It sets a new state-of-the-art benchmark on tests of real-world software engineering, such as SWE-bench Verified, demonstrating superior performance against preceding models in fixing software bugs. This technical capability is paired with stunning token efficiency. Anthropic’s documentation shows that in specific high-complexity tasks, Opus 4.5 uses up to 76% fewer output tokens than older models in the Opus and Sonnet families to achieve the same result. This efficiency is crucial for developers building agentic workflows—AI programs designed to act independently—as it fundamentally reduces both latency and operational expense.

To give users ultimate control over this balance of speed and depth, Anthropic introduced the Effort Parameter. This allows developers to specify if they require "low" effort (the fastest and most token-efficient response for high-volume automation) or "high" effort (maximum thoroughness and depth of reasoning for complex analysis). This adjustable control over the model's internal process allows businesses to precisely tailor the AI's performance to the exact needs and budget of any given task. The model maintains a generous 200,000-token context window, which is ample for deep document research. Furthermore, the model features refined context management, automatically summarizing and prioritizing earlier conversation history, leading to highly consistent performance in long user sessions and through key integrations like Claude for Excel and various IDE partners.