The author reruns security vulnerability triage experiments across 26 combinations of Claude and GPT-5 models with varying reasoning effort and context sizes. A four-model “council” achieved 86.2% unanimous votes, and GPT-5.4 at medium/high effort led overall performance, though full-chain solutions remained rare. The study also found higher reasoning sometimes backfires and function-level inputs outperformed whole-file analysis.

• More reasoning effort doesn't reliably mean better vulnerability detection—GPT-5.5-high/xhigh underperformed its own medium setting, and low-effort modes always trailed.
• A 4-model voting council hit 86.2% unanimous decisions and cut no-majority cases to 2.8%, far outperforming any single model.
• Feeding models function-level code instead of whole files dramatically boosted results.
• Even the best setups rarely solved the full vulnerability chain (just 1.9% full solves overall), showing partial pattern-matching rather than genuine deep understanding.

This article reruns a 2023 benchmark with the latest LLMs, comparing direct SQL generation against querying through a structured dbt Semantic Layer. It finds that while text-to-SQL accuracy has jumped, a modeled Semantic Layer still delivers near-perfect, deterministic results for covered queries, making it ideal for complex or critical use cases.

• Top 2026 LLMs (Opus 4.6, Sonnet 4.6, GPT-5.3 Codex, GPT-5.2) now hit 80%+ accuracy on raw text-to-SQL, up sharply from the GPT-4 era's ~60%
• A fully modeled dbt Semantic Layer still reaches near-100% accuracy on in-scope queries, versus ~95% for a minimal semantic layer and ~60-80% for plain text-to-SQL
• Data modeling matters more than model choice: basic modeling boosted accuracy 15-20 points, while swapping between top-tier LLMs or reasoning settings only shifted results by single digits
• Semantic layers pay off for complex, messy, enterprise-scale data, while raw LLM text-to-SQL remains a fine low-setup option for one-off or small-dataset analysis