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GLM-5-Turbo Explained: agent-first base model for “Lobster” (OpenClaw) workflows(2026 Guide)

GLM-5-Turbo is a next-generation large language model released by Zhipu AI in March 2026, optimized specifically for “lobster” agent environments (OpenClaw ecosystem). It is a high-speed, agent-focused variant of GLM-5 designed for long-chain task execution, tool calling, and enterprise-grade AI automation. It features a ~200K token context window, Mixture-of-Experts architecture, and improved stability in multi-step agent workflows.

GLM-5 vs GLM-4.7: what changed, what matters, and should you upgrade?

GLM-5, released February 11, 2026 by Zhipu AI (Z.ai), represents a large architectural leap from GLM-4.7: bigger MoE scale (≈744B vs ~355B total params), higher active parameter capacity, lower measured hallucination, and clear gains on agentic and coding benchmarks — at a cost in inference complexity and (sometimes) latency.

Qwen 3.5 vs Minimax M2.5 vs GLM 5: Which is Better in 2026

Qwen 3.5 targets large-scale, low-cost agentic multimodal workloads with a sparse Mixture-of-Experts (MoE) design and massive activated capacity; Minimax M2.5 emphasizes cost-efficient, realtime agent throughput at low running costs; GLM-5 focuses on heavy reasoning, long-context agents and engineering workflows via a very large MoE-style architecture optimized for token efficiency. The “best” depends on whether you prioritize raw reasoning/coding quality, agent throughput and cost, or open-source flexibility and long-context engineering workflows.

Feb 12, 2026

GLM-5: Feature, Performance benchmarks and Access

The release of GLM-5, unveiled this week by China’s Zhipu AI (branded publicly as Z.AI / zai-org in many developer channels), marks another step in the accelerating cadence of large-model releases. The new model is being positioned as Zhipu’s flagship: larger in scale, tuned for long-horizon agentic tasks, and built with engineering choices intended to reduce inference cost while preserving long context. Early industry reporting and hands-on developer notes suggest meaningful gains in coding, multi-step reasoning and agent orchestration compared with previous GLM iterations — and in some tests it even challenges Claude 4.5.