GLM-4.7 represents a deliberate shift in the GLM family toward agentic coding workflows—situations where a model must autonomously navigate complex, multi-file programming tasks across extended sessions. Built upon its predecessor GLM-4.6, this model was engineered with a 355-billion parameter architecture featuring 32 billion active parameters during inference, allowing it to handle the intricate demands of real development environments. Its 200K-token context window gives it room to reason across large codebases without losing the thread of a complex task, while its design specifically targets terminal-based execution rather than simple chat interactions. The model also notably preserves its thinking blocks across conversation turns, a crucial feature for maintaining coherent reasoning during long coding sessions where context continuity determines success.

The improvements in GLM-4.7 translate into measurable advantages on the benchmarks that matter most for development work: it achieves 73.8% on SWE-bench, competitive with Claude Sonnet 4.5 on real-world software engineering tasks, while reaching 87.4% on τ²-Bench and 84.9% on LiveCodeBench for tool use and coding evaluation. These results position it as the top-ranked open-source model in WebDev and a viable alternative to proprietary models for developers who need reliable performance in production. The model's MIT license and open-weight availability lower the barrier for integration into custom agentic pipelines, and its stability when interacting with external tools makes it particularly well-suited for CI/CD contexts and automated development workflows. Beyond pure coding, GLM-4.7 also produces coherent output for conversational, writing, and role-playing scenarios, suggesting a flexible foundation that can serve both specialized development agents and broader interactive applications.