267 Rising Performance of Open-Source Models + Falling Token Costs = Explosion of Usage by Developers Using AI Closed-source models – like GPT-4, Claude, or Gemini – have dominated usage among consumers and large enterprises, largely because of their early performance advantage, ease of use, and broader awareness. These models came bundled in clean, productized interfaces and offered reliable outputs with minimal setup. For enterprises, they promise security and ease-of-use for non-technical employees. For consumers, they came with name recognition, fast onboarding, and polished UX. That combination has kept closed models at the center of the AI mainstream. But performance leadership is no longer a given. Open-source models are closing the gap – faster than many expected – and doing so at a fraction of the cost to users. Models like Llama 3 and DeepSeek have demonstrated competitive reasoning, coding, and multilingual abilities, while being fully downloadable, fine-tunable, and deployable on commodity infrastructure. For developers, that matters. Unlike enterprise buyers or end-users, developers care less about polish and more about raw capability, customization, and cost efficiency. And it is developers – more than any other group – who have historically been the leading edge of AI usage. The recent trend appears increasingly clear: more developers are gravitating toward low-cost, high-performance open models, using them to build apps, agents, and pipelines that once required closed APIs. Time will tell if that advantage scales beyond the developer ecosystem. Many open-source tools still lack the brand power, plug-and-play user experience (UX), and managed services that drive adoption among consumers and large organizations. But as the cost-performance ratio of open models continues to improve – and if the infrastructure to support them becomes more turnkey – those advantages could start to spread beyond the developer community.