Claude Haiku 4.5 + Skills Outperforms Opus 4.7: A Revolutionary Blueprint for Model Routing
research#llm📝 Blog|Analyzed: Apr 22, 2026 21:19•
Published: Apr 22, 2026 19:02
•1 min read
•Zenn ClaudeAnalysis
This fascinating experiment demonstrates a massive leap in AI efficiency, showing that smaller Large Language Models (LLMs) like Claude Haiku 4.5 can surpass the heavyweight Opus 4.7 when equipped with specialized Agent skills. By utilizing Prompt Engineering to create structured 'training wheels,' developers can achieve state-of-the-art results while drastically cutting API costs and Latency. This shift in perspective opens up incredible opportunities for businesses to optimize their Generative AI workflows without sacrificing quality.
Key Takeaways
- •Benchmark tests across 84 tasks and over 7,000 trials showed Claude Haiku 4.5's score jumping from 61.2% to a staggering 84.3% when augmented with skills.
- •Implementing skills works via three powerful mechanisms: prompt compression, procedure fixation, and explicitly defining evaluation criteria.
- •This approach acts as 'training wheels' for smaller models, enabling highly efficient routing for routine tasks while reserving heavy inference for complex, novel domains.
Reference / Citation
View Original"SkillsBench（84タスク / 7モデル / 7,308試行）で 61.2% → 84.3%、Opus 4.7（80.5%）を上回った。"