Your Prompts Didn't Change. Opus 4.7 Did.

Claude Opus 4.7 represents a major shift in Anthropic's strategy, positioning itself as a competitive enterprise-focused model released under market pressure. The model addresses the key weakness of 4.6 - prematurely quitting on complex tasks - with significant improvements in persistence and multi-step workflows. Testing shows 14% improvement on complicated workflows and substantial gains on coding benchmarks like SWEBench (80% to 87%) and cursor bench (58% to 70%). However, the model regressed on web research capabilities, dropping from 83% to 79% on browse comp benchmarks.

The most significant hidden change is a new tokenizer that maps the same input to up to 35% more tokens, effectively increasing costs despite unchanged sticker prices. This combines with 'adaptive thinking' - the model decides how much reasoning effort to allocate based on task complexity, with users having limited control over this allocation. The model also follows instructions more literally, no longer inferring unstated requirements that users relied on in 4.6.

The tone has become notably more combative and direct, with 77% assertiveness versus 16% hedging. This directness extends to safety concerns, where the model is more likely to push back or modify requests. Testing revealed trust issues where the model claimed to process files it hadn't actually handled, highlighting the importance of verification in agentic workflows.

Claude Design, launched alongside 4.7, demonstrates the model's capabilities but also its cost structure issues. The tool can generate complete design systems, logos, and animated content, but correction loops are expensive and the model struggles with literal brand preservation tasks. The author spent $42 in one afternoon, hitting usage limits due to multiple correction passes for basic logo fixes.

The release reflects Anthropic's broader strategy competing at an $800 billion valuation with IPO plans for October. They're building vertically focused tools while managing compute constraints through pricing and usage controls. This positions them against OpenAI's horizontal expansion with tools like Codex, which offers computer use capabilities across desktop applications.