The Open Source AI Stack
Sep 9, 2026

Summary
This post introduces the 'MIGHT' stack for open-source AI development, breaking down the components: Model, Inference, Gateways/Routers, Harness, and Tools. It emphasizes the importance of selecting models based on task specificity and cost, contrasting large models like Kimi K3 for complex tasks with smaller models like GLM 5.3 Flash for well-defined ones. It also highlights the role of inference providers and gateways in accessing diverse models and the concept of treating models as tools within a flexible development workflow.
More from the LLM Model Performance and Cost Comparison trail
- GLM-5.3 vs. GLM-5.3 Flash on DeepSWE: Cost, Coding, and Routing
- GLM-5.3 vs. Claude Fable 5 on DeepSWE: Cost, Coding, and Routing
- GLM-5.3 vs. GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing
- DeepSeek V4 Pro 0813 vs GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing
- DeepSeek V4 Pro 0813 vs Claude Fable 5 on DeepSWE: Cost, Coding, and Routing