Claude Code Best Practices for Opus 4.7: Delegation, xhigh Effort, and Adaptive Thinking

How to Get the Most Out of Claude Opus 4.7 in Claude Code

On April 16, 2026, Anthropic published a comprehensive best-practices guide on the Claude blog for using Claude Opus 4.7 effectively inside Claude Code. The guide reflects a meaningful shift in how Anthropic positions the tool: not as a conversational pair programmer, but as a capable engineer that developers delegate complete tasks to.

Treat Claude as a Delegated Engineer, Not a Pair Programmer

The central framing of the guide is a shift in mental model. Rather than iterating back and forth with brief prompts, Anthropic recommends front-loading full context at the start of a session: intent, constraints, acceptance criteria, and relevant file locations. This reduces the number of clarification cycles Opus 4.7 needs, and produces better outputs when the first turn contains everything the model needs to proceed without interruption.

The guide also recommends enabling push notifications so Claude Code can alert the user when a long-running task completes — allowing developers to step away and return to results rather than monitor progress live.

Effort Levels: Choosing the Right Setting

Opus 4.7 introduces a new xhigh effort tier as the default, but the guide provides clear guidance on when to adjust:

Effort Level	When to Use
low / medium	Cost- or latency-sensitive tasks; still outperforms Opus 4.6
high	Concurrent sessions where cost matters
xhigh (default)	Most coding and agentic work — strong autonomy, good throughput
max	Genuinely hard problems; expect diminishing returns

Anthropic notes that xhigh hits the sweet spot for most production coding use cases. Developers can also influence reasoning intensity through prompting: "Think carefully and step-by-step" pushes toward deeper reasoning; "Prioritize responding quickly" pulls back.

Adaptive Thinking Replaces Fixed Budgets

Fixed thinking budgets are gone in Opus 4.7. The model now uses adaptive thinking, deciding dynamically at each step whether extended reasoning adds value. This makes responses faster on simple queries and more deliberate on complex ones, without requiring any configuration from the developer. Anthropic notes the model is also less prone to overthinking than Opus 4.6 — a practical improvement in agentic contexts where unnecessary reasoning chains previously introduced latency and cost.

What Changed from Opus 4.6

Three behavioral differences are called out explicitly for teams migrating from Opus 4.6:

Response length calibration. Opus 4.7 produces shorter answers for simple queries and longer ones for complex analysis. Developers who need consistent output length should specify it explicitly in their prompts rather than relying on default behavior.

Reduced tool usage. The model reasons more internally rather than calling tools frequently. Workflows that rely on aggressive file reading or tool invocations should prompt for that behavior explicitly — it will not happen by default.

Fewer subagents. Opus 4.7 is more conservative about spawning parallel subagents. Teams that want parallel task execution should explicitly request it, specifying that work should be fanned out across files or independent items.

Where Opus 4.7 Excels

The guide identifies task categories where Opus 4.7 outperforms its predecessor most noticeably: complex multi-file changes, ambiguous debugging scenarios, code review across services, and multi-step agentic workflows that previously required frequent supervision. The recommendation is to let the first turn run as far as possible with complete initial context, rather than intervening early.