Z.ai has introduced GLM-5.3-Flash, the first natively multimodal model in its GLM-5 series. The model has 320 billion total parameters, with 18 billion active parameters. Z.ai says it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price. The company also says it approaches Claude Opus 4.8 on coding and agentic benchmarks. Building on those performance claims, GLM-5.3-Flash combines sparse and linear attention in a hybrid architecture. Z.ai says the design lowers long-context serving costs while retaining precise long-context capabilities. The model also uses Manifold-Constrained Hyper-Connections, or mHC, to improve scaling efficiency. Its pre-training used a 30-trillion-token multimodal corpus. For million-token contexts, Z.ai introduced IndexPool to reduce indexer latency and memory overhead. The technique compresses four indexer key vectors into one through weighted pooling. Before the release, Z.ai tested the model anonymously as Ox Alpha on OpenCod...