GLM

Martin Alderson's argument for why open-weights models like GLM 5.2 will compress frontier lab margins is sparking debate on HN. Here is what the thesis actually says, where HN agrees and disagrees, and why it matters for developers choosing models.

DeepSeek, Kimi, and GLM are cheap enough to run as sidecar subagents for drafts and exploration. The catch is that cheap work you cannot inspect is just expensive noise. A shared canvas makes the output reviewable.

A companion guide to the GLM 5.2 video: an open-weight model positioned against GPT-5.5, walked through with benchmarks, pricing, and a live OpenCode demo. Here is what the video covers and where to go deeper.

GLM-5.2 ships under an MIT license, so it is hosted everywhere - and a few places run it for free or nearly free right now. Here is every way to access Z.ai's open-weights coding model, from OpenCode Go referral credits and Devin to the cheapest per-token routes on OpenRouter, Fireworks, and DeepInfra, plus local Ollama.

Z.ai's GLM-5.2 lands as a 753B open-weights coding model that beats GPT-5.5 on SWE-bench Pro for roughly one-sixth the per-token cost. Here is the real cost math, a worked cost-per-task example, and a when-to-use-which decision guide.

A data-rich, source-cited comparison of the three open-weights coding models that matter in 2026: GLM-5.2, DeepSeek V4, and Qwen3. Benchmark table, per-token pricing, context windows, self-host footprint, and a clear pick-X-if decision matrix.

Z.ai shipped GLM-5.2 in mid-June with a usable 1M-token context window, two thinking-effort levels, and MIT open weights now released. Here is the setup guide for Claude Code, pricing breakdown, and what to test before the benchmarks arrive.