GLM-5 vs. Composer 2.5

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.

Composer 2.5 is Cursor’s newest AI-powered coding model, designed to significantly improve software development productivity through stronger reasoning, enhanced collaboration, and better handling of complex engineering tasks. Compared to Composer 2, the new release delivers major gains in sustained coding performance, allowing developers to work on larger and more complicated projects with improved reliability. The model was trained using expanded compute resources, more advanced reinforcement learning environments, and additional optimization techniques focused on both intelligence and usability. Cursor also refined behavioral aspects of the AI, including communication style and effort calibration, to make interactions feel more natural and productive during real-world coding sessions. A major feature of Composer 2.5 is its targeted reinforcement learning system with textual feedback, which provides localized corrections during training when the model makes mistakes such as invalid tool calls or style violations. This approach helps the AI understand exactly where errors occur and improves its decision-making more effectively than broad reward signals alone. The company further strengthened the model by training it on 25 times more synthetic coding tasks than Composer 2, exposing it to a wider range of difficult engineering challenges and edge cases. These synthetic tasks included feature deletion exercises where the model had to reconstruct missing functionality in real codebases using automated tests as validation signals. During large-scale training, Composer 2.5 demonstrated advanced problem-solving capabilities by reverse-engineering cached data and decompiling Java bytecode to recover deleted APIs in synthetic environments. Cursor also implemented sophisticated distributed training systems such as Sharded Muon and dual mesh HSDP, allowing efficient optimization across extremely large AI models and infrastructure clusters.