2 months ago
- Small AI models are now as smart as prior flagship models, enabling new strategies for agentic analytics that prioritize speed and cost over raw intelligence.
- OpenAI's GPT 5.6 Luna, with an 80% price cut, achieves 99.8% accuracy on SQL benchmarks at a 5x lower price than Gemini-3-Flash, and even 10x cheaper on semantic modeling benchmarks due to model caching improvements.
- Luna occupies a new section of the price/performance curve, offering speed and cost efficiency for tasks that don't require maximum intelligence, such as SQL generation.
- Practical steps include switching to Luna on max effort for low-intelligence tasks and asking more questions (Jevons Paradox) to leverage the technology's improved performance.
- Larger shifts involve adopting faster analytical databases to avoid latency bottlenecks, building detailed context layers for weaker models, and running frequent evals with natural language questions to catch regressions and adapt to business changes.
- Eval workflows should be lab-agnostic to avoid lock-in and allow quick switching to optimal models as prices and performance evolve.
- The text introduces Guides, MotherDuck's context layer for AI agents, to convert raw intelligence into accurate answers with lower token spend.