# GLM-5.2 and IndexShare for Long-Context Sparse Attention

> Source: <https://sebastianraschka.com/blog/2026/glm-5-2-indexshare.html>
> Published: 2026-06-18 09:16:05+00:00

# GLM-5.2 and IndexShare for Long-Context Sparse Attention

[GLM-5.2](https://huggingface.co/zai-org/GLM-5.2) is a recent open-weight model release from Z.ai. My first impression is that it is the best open-weight model today. As usual for fresh releases, I would treat the release-time leaderboard position as date-sensitive.

Architecture-wise, it builds on the earlier GLM-5 and GLM-5.1 architecture. In particular, it reuses [Multi-head Latent Attention](/llm-architecture-gallery/mla/) and [DeepSeek Sparse Attention](/llm-architecture-gallery/deepseek-sparse-attention/), the DSA mechanism from DeepSeek V3.2 that I covered in the [DeepSeek V3 to V3.2 article](https://magazine.sebastianraschka.com/p/technical-deepseek).

What’s new is IndexShare. This is a cross-layer reuse trick for DSA. Instead of recomputing the sparse-attention top-k indexer in every layer, GLM-5.2 runs the full indexer only once every four layers. The following layers then reuse the selected token indices.

This keeps the same DSA idea but makes 1M-token inference cheaper. The attention pattern is still adaptive, but the model spends less work repeatedly deciding which earlier tokens to attend to.

The local [GLM-5.2 architecture card](/llm-architecture-gallery/#card-glm-5-2) has the current summary, config links, and [benchmark](/glossary/#benchmark) references.

By the way, in the [Artificial Analysis Coding Index](https://artificialanalysis.ai/) snapshot below, GLM-5.2 scores 68.8 versus 56.7 for Claude Opus 4.8 (max), more than 10 points higher on coding benchmarks. That’s super impressive for a fresh open-weight model.

Source: lightly edited website version of my [Substack note](https://substack.com/@rasbt/note/c-278515750).

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