An open-weights AI model just beat GPT-5.5 on key coding benchmarks, and it costs roughly a sixth of what OpenAI charges. Z.AI’s GLM-5.2, unveiled between June 13 and 16, is designed for the kind of long, complex coding tasks that separate toy demos from production-ready tools.
The model edges out GPT-5.5 by approximately 1% on FrontierSWE and ranks first among all open-source models on long-horizon coding benchmarks. It also posts strong numbers on PostTrainBench and SWE-Marathon, two benchmarks that test a model’s ability to handle multi-step engineering problems that unfold over extended interactions.
What GLM-5.2 actually brings to the table
GLM-5.2 uses a Mixture-of-Experts (MoE) architecture. Instead of running all 744 to 753 billion parameters on every query, it activates roughly 40 billion at a time, routing each task to the specialists best equipped to handle it.
The context window jumped to 1 million tokens from the 200,000 tokens available in the predecessor GLM-5.1. For reference, 1 million tokens is roughly the equivalent of feeding the model an entire large codebase and asking it to reason about the whole thing at once.
