Everything on Model Efficiency

Small models (9.3B) can match larger models in specific domains through focused training and data curation. Efficiency enables on-device deployment.

Impact: Lowers inference costs and expands market reach by enabling deployment on consumer hardware and edge devices.

— from Ideogram Open Weights Model Drives Enterprise Customization · AI + a16z· Jun 15, 2026

GPT 5.5 exhibits extended "thinking" phases, which can result in long processing times for simple tasks, making it less efficient for basic "vibe coding" compared to complex problem-solving.

Impact: Users should match model complexity to problem difficulty; deploying GPT 5.5 for simple tasks may incur unnecessary latency and costs without proportional value gains.

— from GPT 5.5: Advanced Autonomy, Tech Debt Resolution, and High-Cost Intelligence · How I AI· Apr 23, 2026