Best practices for using Claude Opus 4.7 with Claude Code

2026-04-16 · Source: Claude Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

Anthropic has released Opus 4.7, their most capable generally available model for coding, enterprise workflows, and long-running agentic tasks, which handles ambiguity better, excels at bug finding and code review, and maintains context across sessions more reliably than Opus 4.6. This update introduces an updated tokenizer and a tendency for the model to think more at higher effort levels, particularly in later turns of longer sessions, impacting token usage. To optimize performance, users may need to adjust prompts and harnesses. The default effort level for Opus 4.7 in Claude Code is now "xhigh", a new setting between "high" and "max" designed to balance reasoning and latency for intelligence-sensitive tasks like API design and code migration. Opus 4.7 also features adaptive thinking, allowing the model to decide when to invest more thought, and exhibits calibrated response lengths and more judicious tool use compared to its predecessor.

Key takeaway

For AI Engineers optimizing Claude Code setups, you should recalibrate your approach to Opus 4.7 by treating it as a capable delegate rather than a pair programmer. Prioritize specifying full task context in the initial turn and leverage the new "xhigh" default effort level for most agentic coding, especially for complex tasks like API design or code migration, to balance intelligence and token efficiency.

Key insights

Opus 4.7 optimizes agentic coding through adaptive thinking, calibrated effort levels, and refined interaction patterns.

Principles

• Specify tasks upfront for stronger outputs.
• Reduce user interactions to minimize reasoning overhead.
• Calibrate effort levels to task complexity and cost sensitivity.

Method

Structure interactive coding sessions by specifying tasks in the first turn, batching questions, and using auto mode for trusted execution. Adjust effort levels based on task intelligence and cost sensitivity, with "xhigh" as the recommended default.

In practice

• Use "xhigh" effort for API design and code review.
• Prompt for more or less thinking to control token usage.
• Explicitly guide tool use for aggressive search or file reading.

Topics

• Claude Opus 4.7
• Claude Code
• Effort Levels
• Adaptive Thinking
• Interactive Coding Sessions

Best for: AI Engineer, Machine Learning Engineer, Prompt Engineer

Related on AIssential

• Claude's Opus 4.7 Review — Claude Opus 4.7 significantly improves agentic coding, instruction following, and multimodal understanding.
• v2.1.109 — Claude Code updates enhance security, user experience, and model capabilities, notably with Opus 4.7 xhigh and improved CLI.
• 6 Practical Tips to Use Opus 4.7 in Claude Code More Efficiently — Optimizing Claude Opus 4.7 performance requires careful workflow configuration beyond model capabilities alone.
• 4 New Techniques to Maximize Claude Code — Optimizing coding agent interaction through specific techniques significantly boosts developer productivity and output quality.
• Meet the winners of the Built with Opus 4.7 Claude Code hackathon — Claude Code and Opus 4.7 enable rapid development of complex, multi-agent AI applications across diverse domains, even for non-programmers.
• v2.1.53 — Claude Code has undergone significant evolution across recent versions, introducing advanced AI models like Opus 4.6, Sonnet 4.6, and Haiku 4.5, featuring 1M context windows and configurable "effort levels".

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Editorial summary, takeaway, and curation by AIssential. Original article published by Claude Blog.