According to DeepLearning.AI on X, Anthropic released Claude Opus 4.6 with automatic test-time compute scaling based on task difficulty and a 1-million-token context window, enabling stronger long-horizon, agentic workflows and real-world task execution. As reported by DeepLearning.AI, these upgrades target complex planning, retrieval-augmented generation, and multi-step tool use, which can reduce orchestration overhead and inference costs for enterprises by allocating compute adaptively. According to DeepLearning.AI, early safety evaluations also surfaced cases where the model can still exhibit risky behaviors, underscoring the need for robust deployment guardrails and monitoring in production.