技术规格

Claude Opus 4.6

Claude Opus 4.6 represents the pinnacle of Anthropic's intelligence-first model hierarchy, engineered specifically for high-stakes professional workflows and complex agentic autonomy. As a multimodal foundation model, it processes and synthesizes diverse data types including text, code, and high-resolution visual inputs. The architectural design prioritizes sustained logical consistency and self-correction, enabling the model to manage long-horizon tasks such as end-to-end software engineering and multi-step financial modeling with minimal human intervention. By incorporating advanced planning mechanisms, the model identifies potential execution blockers and revisits its internal reasoning paths before finalizing outputs.

A defining technical advancement in this version is the introduction of an adaptive thinking framework, which replaces static reasoning configurations with dynamic effort levels. This system allows the model to autonomously calibrate its internal chain-of-thought depth based on the perceived complexity of the prompt. Developers can manually tune this behavior through four distinct effort control levels, low, medium, high, and max, providing a programmable interface to balance computational intensity against response latency and cost. This granular control is particularly effective for managing the token economics of agentic sessions where reasoning overhead varies significantly between tasks.

The model's ingestion capacity is facilitated by a million-token context window, supported by a server-side context compaction feature that automatically manages long-running conversation state. This mechanism utilizes intelligent summarization to replace aging context as the session approaches the token limit, ensuring that critical task information remains within the active attention span. Furthermore, the expansion of the output ceiling to 128,000 tokens permits the generation of extensive technical documentation, entire source code modules, and comprehensive legal briefs in a single inference pass, eliminating the need for complex client-side message chaining.