OPT vs. GLM-5

Large language models, which often demand significant computational power and prolonged training periods, have shown remarkable abilities in performing zero- and few-shot learning tasks. The substantial resources required for their creation make it quite difficult for many researchers to replicate these models. Moreover, access to the limited number of models available through APIs is restricted, as users are unable to acquire the full model weights, which hinders academic research. To address these issues, we present Open Pre-trained Transformers (OPT), a series of decoder-only pre-trained transformers that vary in size from 125 million to 175 billion parameters, which we aim to share fully and responsibly with interested researchers. Our research reveals that OPT-175B achieves performance levels comparable to GPT-3, while consuming only one-seventh of the carbon emissions needed for GPT-3's training process. In addition to this, we plan to offer a comprehensive logbook detailing the infrastructural challenges we faced during the project, along with code to aid experimentation with all released models, ensuring that scholars have the necessary resources to further investigate this technology. This initiative not only democratizes access to advanced models but also encourages sustainable practices in the field of artificial intelligence.

GLM-5 is Z.ai’s most advanced open-source model to date, purpose-built for complex systems engineering, long-horizon planning, and autonomous agent workflows. Building on the foundation of GLM-4.5, it dramatically scales both total parameters and pre-training data while increasing active parameter efficiency. The integration of DeepSeek Sparse Attention allows GLM-5 to maintain strong long-context reasoning capabilities while reducing deployment costs. To improve post-training performance, Z.ai developed slime, an asynchronous reinforcement learning infrastructure that significantly boosts training throughput and iteration speed. As a result, GLM-5 achieves top-tier performance among open-source models across reasoning, coding, and general agent benchmarks. It demonstrates exceptional strength in long-term operational simulations, including leading results on Vending Bench 2, where it manages a year-long simulated business with strong financial outcomes. In coding evaluations such as SWE-bench and Terminal-Bench 2.0, GLM-5 delivers competitive results that narrow the gap with proprietary frontier systems. The model is fully open-sourced under the MIT License and available through Hugging Face, ModelScope, and Z.ai’s developer platforms. Developers can deploy GLM-5 locally using inference frameworks like vLLM and SGLang, including support for non-NVIDIA hardware through optimization and quantization techniques. Through Z.ai, users can access both Chat Mode for fast interactions and Agent Mode for tool-augmented, multi-step task execution. GLM-5 also enables structured document generation, producing ready-to-use .docx, .pdf, and .xlsx files for business and academic workflows. With compatibility across coding agents and cross-application automation frameworks, GLM-5 moves foundation models from conversational assistants toward full-scale work engines.