On standard coding benchmarks, GLM-5.2 is the strongest open-source model, improving on GLM-5.1 by a wide margin: 81.0 vs. 63.5 on Terminal-Bench 2.1 and 62.1 vs. 58.4 on SWE-bench Pro. It also closes much of the gap to the closed-source frontier — on Terminal-Bench 2.1 (81.0) it lands within a few points of Claude Opus 4.8 (85.0) — while staying ahead of Gemini 3.1 Pro.

GLM-5.2 also introduces effort level control, enabling users to explicitly balance model capability against task execution speed and computational cost. As shown in the figure, GLM-5.2 delivers substantially stronger agentic coding performance than GLM-5.1 at comparable token budgets, with its capability roughly positioned between Claude Opus 4.7 and Claude Opus 4.8 under similar token consumption. Moreover, the Max effort level allows users to allocate additional computation when higher performance is required in challenging tasks, further extending the model’s coding capability. This design gives users greater flexibility when using GLM-5.2 for coding tasks, allowing them to select the most suitable reasoning mode for different scenarios.

To support 1M context length, in GLM-5.2, we apply IndexShare to reduce the computational cost of the indexer in DSA. Specifically, in GLM-5.2, every 4 transformer layers share a lightweight indexer. The indexer is placed at the first of 4 layers and topk indices are used for 4 layers. This reduces the computation of indexer dot product and topk operation in 3/4 layers. GLM-5.2 is trained with IndexShare from mid-training with 128K sequence length, outperforming GLM-5.1 on long-context benchmarks with less computation.

We improve the MTP layer of GLM-5.2 for speculative decoding with two objectives: 1) Minimize the cost of the MTP layer as draft model; 2) Maximize the acceptance rate of speculative decoding.