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Zhipu AI Launches GLM-5-Turbo, a Practical Foundation Model Optimized for Lobster Intelligence
Zhipu AI has officially launched its new foundational model, GLM-5-Turbo. This model has been fundamentally redesigned for the "Lobster" (OpenClaw) open-source agent ecosystem, specifically to address the "stalling" issue that general-purpose large models face with long, complex tasks. This evolution transforms AI from a simple productivity tool into a persistent, round-the-clock digital workforce.
Core Evolution: Eliminating Long-Task Stalls
To create a model that is truly "work-ready," the development team rebuilt its training data based on real-world agent workflows, focusing on four key capabilities:
Reliable Tool Calling: Improves the accuracy of calling external tools and skills, preventing failures at critical junctures.
Complex Instruction Breakdown: Accurately identifies objectives and plans sequential steps, enabling efficient multi-agent collaboration.
Temporal Awareness: Optimized for scheduled triggers and long-duration operations, ensuring task continuity over time.
High-Throughput Execution: Enhances the processing efficiency of extended business workflows, leading its category in response stability.
Real-World Validation: Exceptional Results
Zhipu also released the agent evaluation benchmark ZClawBench. In a blind test spanning diverse fields like code development, data analysis, and content creation, the new model (codenamed Pony-Alpha-2) was preferred by 90% of participants. Feedback from testing teams at companies including Alibaba, Meituan, Kuaishou, and ByteDance indicates it holds top-tier competitive advantages in tool-call accuracy, instruction adherence, and controlling "hallucinations" in office environments.
Platform Integration and Security Management
For ecosystem deployment, GLM-5-Turbo has been integrated into SoftBank's MechRevolution "Lobster Box," delivering a native AI hardware experience. Concurrently, Zhipu introduced the enterprise-grade security system "Claw for Enterprise Security." This system uses visual monitoring, permission orchestration, and audit logs to move complex multi-agent collaboration out of the "black box" era, ensuring all data remains securely within the enterprise boundary.
To help manage computational costs, Zhipu has launched the "Lobster Package" with individual and team tiers. GLM-5-Turbo is now available on the Zhipu Open Platform with limited-time benefits: during off-peak hours, quota consumption is charged at a 1x coefficient. This promotion runs until the end of April.
Coding Plan Max: https://z.ai/subscribe
OpenRouter: https://openrouter.ai/z-ai/glm-5-turbo
API: https://docs.z.ai/guides/llm/glm-5-turbo
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Comments (3)
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![GeorgeThomas]()
GLM-5-Turbo專為龍蝦智能生態設計？這命名也太有趣了吧！看來智譜AI這次是鐵了心要解決通用模型在特定任務上『卡殼』的老問題。不過，這種針對性優化會不會讓模型在其他場景的泛化能力變弱啊？有點好奇實際應用效果。
![JasonAnderson]()
Das mit dem Hummer-Ökosystem ist ja echt witzig 🦞. Aber mal ehrlich, wird das jetzt wirklich das 'Stocken'-Problem lösen oder ist das nur Marketing-Blabla? Finde es gut, dass spezialisierte Modelle entwickelt werden, aber der Hype wird mir langsam zu viel. Vielleicht sollte ich mir den OpenClaw-Code mal selbst ansehen, bevor ich urteile.
![ScottPerez]()
La idea de un modelo dedicado a ecosistemas de agentes suena muy práctica, aunque me pregunto si no será otra estrategia para ligar a los usuarios al ecosistema de Zhipu 🤔. La metáfora de la 'langosta inteligente' es curiosa, pero la cuestión clave es si realmente alivia el atasco en tareas complejas. Espero que esté disponible para pruebas pronto... ¡y que no sea solo para grandes empresas! 🦞
Zhipu AI has officially launched its new foundational model, GLM-5-Turbo. This model has been fundamentally redesigned for the "Lobster" (OpenClaw) open-source agent ecosystem, specifically to address the "stalling" issue that general-purpose large models face with long, complex tasks. This evolution transforms AI from a simple productivity tool into a persistent, round-the-clock digital workforce.
Core Evolution: Eliminating Long-Task Stalls
To create a model that is truly "work-ready," the development team rebuilt its training data based on real-world agent workflows, focusing on four key capabilities:
Reliable Tool Calling: Improves the accuracy of calling external tools and skills, preventing failures at critical junctures.
Complex Instruction Breakdown: Accurately identifies objectives and plans sequential steps, enabling efficient multi-agent collaboration.
Temporal Awareness: Optimized for scheduled triggers and long-duration operations, ensuring task continuity over time.
High-Throughput Execution: Enhances the processing efficiency of extended business workflows, leading its category in response stability.
Real-World Validation: Exceptional Results
Zhipu also released the agent evaluation benchmark ZClawBench. In a blind test spanning diverse fields like code development, data analysis, and content creation, the new model (codenamed Pony-Alpha-2) was preferred by 90% of participants. Feedback from testing teams at companies including Alibaba, Meituan, Kuaishou, and ByteDance indicates it holds top-tier competitive advantages in tool-call accuracy, instruction adherence, and controlling "hallucinations" in office environments.
Platform Integration and Security Management
For ecosystem deployment,
To help manage computational costs, Zhipu has launched the "Lobster Package" with individual and team tiers.
Coding Plan Max: https://z.ai/subscribe
OpenRouter: https://openrouter.ai/z-ai/glm-5-turbo
API: https://docs.z.ai/guides/llm/glm-5-turbo
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GLM-5-Turbo專為龍蝦智能生態設計？這命名也太有趣了吧！看來智譜AI這次是鐵了心要解決通用模型在特定任務上『卡殼』的老問題。不過，這種針對性優化會不會讓模型在其他場景的泛化能力變弱啊？有點好奇實際應用效果。
Das mit dem Hummer-Ökosystem ist ja echt witzig 🦞. Aber mal ehrlich, wird das jetzt wirklich das 'Stocken'-Problem lösen oder ist das nur Marketing-Blabla? Finde es gut, dass spezialisierte Modelle entwickelt werden, aber der Hype wird mir langsam zu viel. Vielleicht sollte ich mir den OpenClaw-Code mal selbst ansehen, bevor ich urteile.
La idea de un modelo dedicado a ecosistemas de agentes suena muy práctica, aunque me pregunto si no será otra estrategia para ligar a los usuarios al ecosistema de Zhipu 🤔. La metáfora de la 'langosta inteligente' es curiosa, pero la cuestión clave es si realmente alivia el atasco en tareas complejas. Espero que esté disponible para pruebas pronto... ¡y que no sea solo para grandes empresas! 🦞
