GLM-5 is designed to address the demands of complex systems engineering and long-horizon agentic tasks, marking a significant evolution in the model family. By scaling to 744 billion parameters with 40 billion active parameters, the architecture leverages a mixture-of-experts approach to balance intelligence with efficiency. A key design innovation is the integration of DeepSeek Sparse Attention, which allows the model to maintain a substantial context window while optimizing deployment costs. This focus on structural efficiency makes it a robust choice for developers requiring deep reasoning and reliable tool-calling capabilities in autonomous workflows.
The development of GLM-5 involved training on 28.5 trillion tokens, supported by a novel asynchronous reinforcement learning infrastructure known as slime. This post-training advancement enables more fine-grained iterations, helping to bridge the gap between raw pre-trained competence and high-level excellence in specialized tasks. By achieving strong results on benchmarks like SWE-bench Verified, the model demonstrates a practical strength in coding and agentic execution. As an advancement in the series, it provides a powerful foundation for persistent automation and long-chain execution, positioning it as a competitive tool for users building sophisticated, agent-driven applications.
