Simon Willison just called GLM-5.2 the most powerful text-only open-weights LLM, and he has the receipts

Simon Willison doesn't hand out superlatives. So when he writes that Z.ai's GLM-5.2 is "probably the most powerful text-only open weights LLM," that's worth stopping for. His June 17 evaluation walks through a 753B-parameter Mixture-of-Experts model with 40B active params, a 1M-token context window (up from GLM-5.1's 200K), released under a plain MIT license at roughly $1.40 input / $4.40 output per million tokens on OpenRouter.

Here's the part that should rearrange your priors. Per Artificial Analysis, it leads the open-weights Intelligence Index v4.1 at 51, beating both MiniMax-M3 and DeepSeek V4 Pro, which sit at 44. It ranks #2 on Code Arena WebDev, behind only Claude Fable 5. An MIT-licensed model you can self-host is now within striking distance of the closed coding models for real work, not toy benchmarks.

It's not a clean sweep. Willison's pelican-on-a-bicycle SVG test passed cleanly, but his opossum-on-an-e-scooter probe actually regressed versus GLM-5.1, which tells you the improvement isn't uniform across the capability surface. And it burns more output tokens per task, around 43k, so the cheap per-token price gets partly eaten back by verbosity. If you're paying per token, run your own cost-per-completed-task math before you celebrate.

Why this matters to me as a builder: I've been assuming for months that anything serious has to route through Anthropic or OpenAI, and that the open models were a year behind. A 1M-context, MIT-licensed model that ranks #2 on WebDev breaks that assumption. The licensing is the real story. MIT means you can fine-tune it, embed it in a commercial product, run it air-gapped, and never send a token to a vendor. For regulated work, for anything where data residency is a hard requirement, that's the difference between "we can't use AI here" and "we can."

What to do about it: spin GLM-5.2 up on OpenRouter this week and run it against your actual eval suite, not the public leaderboards. Measure cost-per-completed-task, not cost-per-token, because that 43k output number will surprise you. And if you've got a use case blocked on data residency, this is the model to prototype with. It connects directly to the OpenCode story below, the model is becoming a part you plug in, and that changes what your harness needs to be.