Claude Sonnet 4 vs Claude Opus 4.7

Claude 4 Sonnet, released by Anthropic in May 2025, is the mid-tier model in the Claude 4 family, designed to balance capability, cost, and speed. It is multimodal, accepting both text and images, and extends beyond prior versions with improved “computer use” support, allowing API-driven interaction with desktop-like interfaces. By default, it supports 200,000 tokens of context, but as of August 2025, it also offers a 1 million-token context window in public beta—making it one of the most context-capable models available for processing entire codebases or large document sets in a single request.

Sonnet 4 is significantly cheaper than the flagship Opus while still demonstrating strong reasoning, coding, and instruction-following ability with reduced hallucinations. Its extended context capabilities and lower latency make it well-suited for enterprise-scale knowledge management, software development, research assistants, and productivity automation where both cost efficiency and high reliability are essential.

Claude Opus 4.7 is a proprietary multimodal language model developed by Anthropic, released on April 16, 2026. It is designed for agentic coding, long-horizon task execution, and enterprise knowledge work. The model supports text and vision inputs and operates with a context window of up to 1,000,000 tokens. It introduces adaptive thinking, which dynamically allocates reasoning based on task complexity, along with configurable effort controls including a new xhigh setting that sits between the existing high and max levels. It achieves 87.6% on SWE-bench Verified and 78.0% on OSWorld-Verified, reflecting strong performance on autonomous software engineering and computer use tasks respectively.

Compared to Claude Opus 4.6, version 4.7 shows improved instruction following and higher reliability in extended agentic tasks. Vision capabilities now support high-resolution inputs up to 2,576px on the long edge (~3.75 megapixels), more than three times the resolution of prior Claude models, enabling finer interpretation of dense diagrams, UI screenshots, and document layouts. These improvements, combined with self-verification on long-running tasks and a new task budget system for controlling agentic loops, make it well-suited for complex software engineering, technical analysis, and multimodal vision workflows.