There's a chart going around: intelligence on the y-axis, cost to run on the x-axis, and a green "most attractive" quadrant in the upper left where high intelligence meets low cost. The takeaway everyone's posting is that the green quadrant is almost entirely open source. DeepSeek, GLM, MiniMax, Kimi, Qwen all show up smart-enough and cheap, while the closed frontier models sit expensive on the right.
It's a real trend and the chart isn't wrong. But read the x-axis label: cost to run is a blended API price. That number answers "what does it cost to call this model through somebody's API," which is a different question from "what does it cost to run this yourself." For those of us who self-host, the second question is the whole point, and the chart quietly hides the answer.
So here's what it skips — measured on the two cards I own.
The catch: you can't self-host the green quadrant
The open models winning that value quadrant aren't small. Take GLM-5.2 — the one everyone points to when they say the open frontier finally caught up. It's coding-first, currently the strongest open weight on the coding benchmarks: a ~744B-parameter MoE with about 40B active per token. And unlike the closed three, the weights are actually MIT-licensed. That's its whole pitch: you can run it yourself, no per-token fee, weights on your own box. The cheap API price (around $1.40 in and $4.40 out per million tokens, roughly a sixth of GPT-5.5) is the headline. But the thing that sets it apart is the other half: you can run it yourself.
