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GLM-4.5V is the preceding iteration in the GLM-V series that laid much of the groundwork for general multimodal reasoning and vision-language understanding. It embodies the design philosophy of mixing visual and textual modalities into a unified model capable of general-purpose reasoning, content understanding, and generation, while already supporting a wide variety of tasks: from image captioning and visual question answering to content recognition, GUI-based agents, video understanding, and long-document interpretation. GLM-4.5V emerged from a training framework that leverages scalable reinforcement learning (with curriculum sampling) to boost performance across tasks ranging from STEM problem solving to long-context reasoning, giving it broad applicability beyond narrow benchmarks. When it was released, it achieved state-of-the-art results on a large collection of public multimodal benchmarks for open-source models.
Features
- Unified vision-language model: handles both images (or other visual inputs) and text for reasoning and generation
- Strong general-purpose performance across tasks: VQA, image captioning, content recognition, document & video analysis, GUI interpretation
- Trained via scalable reinforcement learning with curriculum sampling to improve reasoning, generalization and robustness
- Good balance of size vs performance — more accessible than heavier models but still competitive on many benchmarks
- Open-source distribution — free to use, fine-tune, adapt or extend for custom research and applications
- Suitable for multi-modal applications: content parsing, automated analysis, agentic workflows, and media processing
Project Samples
