Claude Opus 4.7 felt dumber than 4.6. Then I added five lines to my personalisation.

· Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

Initial perceptions suggested Claude Opus 4.7 performed worse than its predecessor, 4.6, producing longer, noisier responses that required more effort to extract key information. However, a controlled test involving identical prompts and preference blocks across both models, with twelve responses scored on specific dimensions, revealed a different outcome. The test indicated that 4.7 was actually engaging in deeper analysis, generating content that 4.6 did not, such as auditing assumptions and challenging existing frameworks. This additional output, initially perceived as bloat, was reinterpreted as rigor when the model was explicitly prompted to provide such depth. The key finding is that Opus 4.7 requires specific prompting to leverage its enhanced analytical capabilities, otherwise its responses may appear less effective than 4.6.

Key takeaway

For prompt engineers or AI engineers observing perceived performance degradation with Claude Opus 4.7 compared to 4.6, your existing prompts may not be fully leveraging the new model's capabilities. You should conduct controlled A/B tests with specific scoring criteria and experiment with explicit instructions in your prompts to elicit deeper, more rigorous analysis from 4.7, transforming perceived "bloat" into valuable insights.

Key insights

Claude Opus 4.7 offers deeper analysis than 4.6, but requires explicit prompting to reveal its full rigor.

Principles

Method

A/B test models with identical prompts and preference blocks, scoring responses on specific dimensions to compare performance.

In practice

Topics

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.