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…