DownloadGLM-5.3-Flash is Z.ai’s natively multimodal model designed for efficient coding, agentic engineering, reasoning, and long-context workloads. It uses a sparse architecture with 320B total parameters and only 18B active parameters, targeting high capability with substantially lower inference costs. The model introduces a hybrid architecture combining sparse and linear attention to reduce long-context serving costs while retaining precise understanding across large inputs. It also uses Manifold-Constrained Hyper-Connections (mHC) to improve scaling efficiency and was pretrained on a 30-trillion-token multimodal corpus. GLM-5.3-Flash supports text and image inputs and is particularly optimized for coding and autonomous agent workloads, approaching larger frontier models on related benchmarks while improving over GLM-5.2. It supports local deployment through SGLang, vLLM, TokenSpeed, and KTransformers and is released under the MIT license.
Features
- 320B total parameters with only 18B active parameters
- Native multimodal text and image understanding
- Hybrid sparse and linear attention architecture
- Optimized for coding and agentic engineering workflows
- Efficient processing of long-context workloads
- Manifold-Constrained Hyper-Connections for improved scaling
- Pretrained on a 30T-token multimodal corpus
- Supports SGLang, vLLM, TokenSpeed, and KTransformers
Project Samples
