{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/69ab3b7c7036d739021982df/6a66974ea7fbf9da141694dd?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"Stop guessing whether a cheaper model can do the job. ","description":"<p>For deeper playbooks and analysis: <a href=\"https://natesnewsletter.substack.com/\" rel=\"noopener noreferrer\" target=\"_blank\">https://natesnewsletter.substack.com/</a></p><p><br></p><p>What's really happening when five very different AI products get collapsed into the label \"Chinese models\"?</p><p><br></p><p>The common story is that Chinese models are simply cheaper, more open, or easier to run locally — but the reality is that price, capability, license, hardware burden, deployment path, and data jurisdiction vary widely.</p><p><br></p><p>In this video, I share the inside scoop on how I evaluate DeepSeek V4 Pro, Kimi K3, GLM 5.2, MiniMax M3, and Qwen.</p><p><br></p><ul><li>Why cheap tokens can still produce expensive finished work</li><li>How open weights, usable licenses, and practical self-hosting differ</li><li>What \"cost per accepted result\" reveals that token price hides</li><li>Where deployment, data path, and jurisdiction change the risk</li><li>How to run a 20-example bakeoff against your own real work</li></ul><p><br></p><p>Operators, builders, and executives should care because the right decision is not \"Chinese model or American model.\" It is which job, which artifact, which deployment path, and which failure mode your organization can accept.</p><p><br></p><p>Subscribe for daily AI strategy and news.</p><p>Hosted on Acast. See acast.com/privacy for more information.</p>","author_name":"Nate B. Jones"}