GLM-5.2 dropped on HuggingFace under MIT license, and multiple practitioners are calling it the strongest open-weight text model available. Simon Willison called it "probably the most powerful text-only open weights LLM." That framing is mostly fair, but the architecture details matter a lot before you size hardware or drop it into a tool loop.

What the architecture actually does

GLM-5.2 is 753B total parameters, but only ~40B activate per token. Each incoming word wakes up the relevant ~40B parameters and ignores the rest. That's MoE (Mixture of Experts) and it means cheaper compute per token at inference time. The catch most people miss: the full 753B still has to sit in GPU memory. People hear "40B active" and size for 40B and nothing loads.

For long context, GLM-5.2 uses two stacked tricks:

• DSA (DeepSeek Sparse Attention, borrowed from [DeepSeek-V3.2]): normally every word attends to every other word, so cost grows with the square of sequence length. DSA runs a cheap scan first to pick the ~2048 most relevant tokens, then does full attention only on those. About 50% cheaper at 128K context with minimal accuracy loss.
• IndexShare (GLM's actual new contribution):** DSA still re-runs that cheap scan every layer. IndexShare reuses the scan result every 4 layers instead. That cuts the indexer's own cost ~75% and per-token FLOPs 2.9x at 1M context. This is the part that's genuinely new.

The benchmark picture

Z.ai's own numbers (no independent re-runs yet): SWE-bench Pro 62.1 vs GPT-5.5's 58.6, both at 400K context. On the Terminal-Bench 2.1 harness, it scores 81.0 vs Claude Opus 4.8's 85.0, a 4-point gap. Jeremy Howard called it at least as good as Opus 4.8 and GPT-5.5 for his text use.

Real catches before you commit

It's free to download, but not free to run. The whole model has to fit in GPU memory, and at full quality that's about 8 high-end datacenter GPUs (8x H200). The cheaper 8x H100 setup doesn't have enough memory and won't load it. The only realistic home option is a maxed-out Mac Studio (256GB+), and even then it's slow, a few words a second.

Two more things the benchmark scores hide. First, it's chatty Simon Willison measured it spending ~43k words of output per task where rivals use 24-37k. You pay per word out, so in an agent that calls itself in a loop, that adds up to a real bill.

Second, Zvi Mowshowitz noticed it often says it *is* Claude, which hints it may have been trained on Claude's output. If that's true, the benchmark scores might be flattering. Nobody's confirmed it either way yet.

No vision support at all, which is a hard blocker for multimodal agent pipelines.

Long-context serving cost is genuinely lower here than on a dense model of comparable capability, but the hardware bar is higher than the "40B active" framing suggests, and the verbosity issue will bite you in agentic loops where output tokens add up fast.

If you've run MoE models in production tool loops before, how did you handle the verbosity problem, prompt engineering or post-processing the outputs?