Z.ai has released GLM-5, a large open-weights foundation language model positioned for long-horizon agent use cases and complex systems-engineering tasks. Available under a permissive open-source license, GLM-5 joins the ranks of high-parameter models with ecosystem support and deployment options that extend beyond proprietary APIs.
Modal, who partnered with Z.ai ahead of public launch to test drive the model, has integrated GLM-5 into its infrastructure, offering an OpenAI API-compatible endpoint alongside published deployment resources.
The model’s scale — approximately 744 billion parameters with a Mixture-of-Experts (MoE) architecture and sparse attention mechanisms — signals a shift in enterprise expectations around context length and multi-stage workflows. GLM-5’s design emphasizes sustained coherence over long sequences, a requirement for agentic chains of reasoning and multi-hour automated processes that go beyond short-form interaction or single-turn question-answering. Independent benchmarks suggest that GLM-5 closes gaps with recent proprietary models in reasoning and coding tasks while retaining an open-weights distribution.
For organizations that prefer to avoid self-hosting, Modal’s integration surfaces a managed endpoint with a standard API interface. This endpoint is compatible with existing AI frontends and frameworks, allowing unitary integration paths for tools like Vercel’s AI SDK and agent frameworks such as OpenCode and OpenClaw. Documentation from Modal details configuration patterns for these frameworks, reflecting common enterprise requirements for SDK-based routing, tooling extensibility, and integration with autoscaling infrastructure.
GLM-5’s open-weights availability under an MIT license reduces barriers to experimentation, fine-tuning, and on-premises deployments. It also positions enterprises to avoid vendor lock-in associated with closed-source proprietary models when longer context and agentic capabilities are central to product or operational goals.
However, the model’s scale and computational demands underscore the importance of careful readiness planning. Enterprises must weigh the infrastructure cost of multi-GPU clusters against performance needs, particularly for latency-sensitive applications. Governance and observability frameworks must similarly evolve to accommodate autonomous or long-horizon agents, ensuring that control planes are in place to monitor model outputs, decision pathways, and integration fidelity within broader AI systems.