Choosing the right LLM for a specific coding task can significantly impact productivity and cost. This article presents a real-world comparison between Claude Fable 5 and GPT-5.6 Sol, based on extensive daily use. The author found that Fable 5 excels at code generation and documentation-driven development, while GPT-5.6 Sol is better suited for research, debugging, and complex reasoning tasks. The key insight is that no single model is best for everything; strategic task allocation can reduce token waste and improve output quality. For teams using multiple LLMs, this practical framework offers a starting point for developing their own selection criteria. The article also touches on cost implications and latency differences.
Hands-on experience comparing Claude Fable 5 and GPT-5.6 Sol for coding tasks, with a framework for task-specific model selection to optimize token usage.