Use Cases
Scenarios where long-horizon GLM models excel: hours-long code engineering, terminal agents, security auditing and long-document understanding — with model recommendations for each.
Scenarios where long-cycle GLM models excel
Large-Scale Implementation
Multi-file functionality beyond a single context window: the model keeps the entire repository state in view, rather than re-reading it every few rounds.
Automated Research
Several hours of experimental cycles, agents need to plan, run, read results, and correct — this is exactly the workload designed to be evaluated by FrontierSWE and PostTrainBench.
Performance Optimization
Complete call graphs, analyzer outputs, and keeping the kernel and system work on the same track as previous attempts are required.
Complex Debugging
Long-term reproducibility tracking, unstable testing, and cross-service failures; truncating the context is the reason for losing bugs.
All the above scenarios can be run on your own hardware — MIT weights, transformers · vLLM · SGLang · xLLM · ktransformers.How to run the GLM model locally →