GLM 5.2 Efficiency and Architectural Innovations

Architectural Efficiency

GLM 5.2 utilizes a 744B parameter Mixture-of-Experts (MoE) architecture featuring 384 experts, where only approximately 40B parameters are active per token. This design allows the model to maintain high performance while reducing the compute required for inference. To manage a 1M token context window without the quadratic cost of standard attention, the model employs a sparse attention mechanism. An "indexer" component identifies relevant tokens before the attention operation occurs, effectively filtering out unnecessary connections.

Compute Optimization

To further reduce overhead, the model implements "index share," a technique that reuses the computed index across four consecutive layers. This approach results in 2.9 times fewer compute operations at the full 1M token context limit. Inference speed is improved through multi-token prediction, which allows the model to guess multiple tokens ahead and verify them in a single pass, increasing the acceptance rate by approximately 20%. Users can also select between "high" and "max" thinking effort modes to adjust the balance between reasoning capability and token consumption based on task complexity.

Performance and Deployment

GLM 5.2 demonstrates strong results in agentic coding tasks, achieving 74.4% on the Frontier SWE benchmark. While the model is text-only and lacks native vision capabilities, its MIT-licensed open-weight status allows for self-hosting on private hardware, provided the user has sufficient compute resources such as H100 GPUs. The model is also available via API at a significantly lower price point than comparable US-based frontier models.