GLM 5

GLM-5 uses a Mixture of Experts (MoE) architecture with ~745B total parameters and 8 active experts per token (~44B active), enabling efficient large-scale reasoning and agentic workflows compared to previous GLM series.

GLM-5 supports a 200K token context window with up to 128K output tokens, making it suitable for extended reasoning and document tasks.

Yes — GLM-5 is explicitly optimized for long-horizon agent tasks and complex systems engineering workflows, with deep reasoning and planning capabilities beyond standard chat models.

Yes — GLM-5 supports function calling, structured JSON outputs, context caching, and real-time streaming to integrate with external tools and systems.

GLM-5 is competitive with top proprietary models in benchmarks, performing close to Claude Opus 4.5 and offering significantly lower per-token costs and open-weight availability, though closed-source models may still lead in some fine-grained benchmarks.

Yes — GLM-5 is released under a permissive MIT license, enabling open-weight access and community development.

GLM-5 is well suited for long-sequence reasoning, agentic automation, coding assistance, creative writing at scale, and backend system design tasks that demand coherent multi-step outputs.

While powerful, GLM-5 is primarily text-only (no native multimodal support) and may be slower or more resource-intensive than smaller models, especially for shorter tasks.