I Tested All 5 Effort Levels of Claude Opus 4.7

2026-04-20 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, quick

Summary

Anthropic recently updated Claude Opus 4.7, introducing a new "effort" parameter with five tiers, including a new `xhigh` setting, which is now the default for coding tasks in Claude Code. This change, unannounced in public release notes, significantly impacts per-task costs, which can vary by 2.7x between the cheapest and most expensive tiers. An analysis of 12 coding problems, ranging from bug fixes to multi-file refactors and agentic tool-calling, was conducted across all five effort levels. The study aimed to determine the actual performance differences and cost implications of each tier, especially given that the previous `high`, `max`, `medium`, and `low` settings likely now have altered meanings or reduced efficacy.

Key takeaway

For MLOps Engineers optimizing LLM inference costs, you should critically evaluate Claude Opus 4.7's new `xhigh` effort tier. While it's the default for coding, its 2.7x higher token cost compared to lower tiers may not yield proportional performance improvements for all tasks. Conduct your own benchmarks on representative workloads to identify the most cost-effective effort setting for your specific applications, avoiding the "max" setting if it proves inefficient.

Key insights

Claude Opus 4.7's new `xhigh` effort tier is the default for coding, but its cost-performance trade-offs vary significantly.

Principles

• Default settings may not be optimal for all use cases.
• Increased computational effort does not guarantee proportional performance gains.

Method

The study involved running 12 diverse coding problems through all five effort tiers of Claude Opus 4.7.

In practice

• Test different effort tiers for specific coding tasks.
• Evaluate cost implications of `xhigh` vs. lower tiers.

Topics

• Claude Opus 4.7
• LLM Effort Parameter
• Coding Problem Solving
• Performance-Cost Trade-offs
• Anthropic API

Best for: CTO, VP of Engineering/Data, MLOps Engineer, AI Engineer, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.