Claude Opus 4.7 (US)

Claude Opus 4.7 is designed as a refined iteration within its model family, prioritizing accuracy, reliability, and objective performance. The architecture is engineered to achieve a 92% honesty rate by reducing sycophancy, ensuring that outputs remain dependable for users who require precision in automated workflows. Beyond its core text processing, the model features an updated tokenizer for improved efficiency and significantly enhanced vision capabilities, allowing it to interpret images at three times the resolution of its predecessor. It is built to handle complex, long-running tasks with rigor, making it a stable and practical choice for professional applications that demand consistency.

The model demonstrates notable advancements in agentic reasoning and software engineering, evidenced by its strong performance on benchmarks like SWE-bench Pro. Its development lineage emphasizes a balance between capability and controlled behavior, incorporating experimental efforts to manage cyber capabilities while maintaining high utility. The model is particularly adept at multi-agent coordination and self-verification, allowing it to independently manage difficult coding tasks that previously required human supervision. By focusing on verifiable outputs and instruction adherence, it serves as a robust tool for users seeking a secure and predictable environment for creative and technical projects.