GLM 5.2 Model Overview and Performance Analysis

Model Architecture and Performance

GLM 5.2 is a 753B parameter Mixture-of-Experts (MoE) model featuring 40 active parameters and a 1M-token context window. It is released under an MIT license and is available on Hugging Face for post-training or commercial deployment. On the Artificial Analysis intelligence index, the model scores approximately 51, trailing slightly behind GPT 5.5 and Claude Fable 5. Despite this, it demonstrates strong performance in long-horizon reasoning tasks, notably outperforming both GPT 5.5 and Claude Fable 5 in the Vending Bench simulation, a benchmark that evaluates a model's ability to manage a business over a simulated year.

Cost and Inference Efficiency

The model provides a cost-effective alternative to closed-source frontier models. According to Artificial Analysis, the weighted average cost per intelligence index task for GLM 5.2 is approximately $0.42, compared to $0.83 for GPT 5.5 X-High. Current market pricing averages $1.40 per million input tokens and $4.40 per million output tokens, though costs vary by inference provider. The model requires higher token usage when "thinking" effort is increased to achieve peak performance, which is a factor to consider for cost-sensitive applications.

Practical Application and Coding

In coding benchmarks like DeepSuite, GLM 5.2 remains slightly behind top-tier models like Claude Code or Codeex, but it maintains a competitive edge in price-to-performance ratios. A live demonstration using OpenCode to generate a single-file SaaS landing page resulted in approximately 700 lines of code. The output included functional animations, hover effects, and interactive pricing toggles, though it exhibited common AI-generated artifacts such as excessive linear gradients and minor layout inconsistencies in UI elements.