The user wants me to rewrite an article about open-source AI models via API pricing. Let me carefully extract all the factual data from the original and rewrite it in a CTO's voice, first-person, with a focus on cost-effectiveness, fast iteration, architecture decisions, scale, ROI, and vendor lock-in avoidance. Let me extract all the key data points I need to preserve exactly: Model pricing (Output): DeepSeek V4 Flash: $0.25/M DeepSeek V3.2: $0.38/M Qwen3-32B: $0.28/M Qwen3-8B: $0.01/M Qwen3.5-27B: $0.19/M ByteDance Seed-OSS-36B: $0.20/M GLM-4-32B: $0.56/M GLM-4-9B: $0.01/M Hunyuan-A13B: $0.57/M Ling-Flash-2.0: $0.50/M Self-host cost estimates (monthly GPU): DeepSeek V4 Flash: $500-2000 DeepSeek V3.2: $800-3000 Qwen3-32B: $400-1500 Qwen3-8B: $200-800 Qwen3.5-27B: $300-1200 ByteDance Seed-OSS-36B: $500-2000 GLM-4-32B: $400-1500 GLM-4-9B: $200-800 Hunyuan-A13B: $300-1000 Ling-Flash-2.0: $300-1000 GPU Server Costs: 7-9B: 1× A100 40GB, $400-800 cloud, $200-400 on-prem 13-14B: 1× A100 80GB, $600-1,200 cloud,…